Donoho, D.L.,
Compressed sensing,
IT(52), No. 4, 2006, pp. 1289-1306.
BibRef
0600
He, L.,
Chen, H.,
Carin, L.,
Tree-Structured Compressive Sensing With Variational Bayesian Analysis,
SPLetters(17), No. 1, January 2010, pp. 233-236.
IEEE DOI
1001
BibRef
Romberg, J.K.[Justin K.],
Compressive Sensing By Random Convolution,
SIIMS(2), No. 4, 2009, pp. 1098-1128.
DOI Link
1002
compressive sensing; random matrices; L_1 regularization
BibRef
Muise, R.[Robert],
Compressive Imaging: An Application,
SIIMS(2), No. 4, 2009, pp. 1255-1276.
DOI Link
1002
compressive imaging; persistent surveillance; image multiplexing
Field of view imaging, field of regard imaging. Limit the area to process
to improve times.
BibRef
Han, B.[Bing],
Wu, F.[Feng],
Wu, D.P.[Da-Peng],
Image representation by compressive sensing for visual sensor networks,
JVCIR(21), No. 4, May 2010, pp. 325-333.
Elsevier DOI
1006
BibRef
Earlier:
Image representation by compressed sensing,
ICIP08(1344-1347).
IEEE DOI
0810
Image representation; Compressive sensing; Random sampling; Projection
onto convex sets; Convex optimization; Image decomposition;
Interpolation; Image reconstruction
BibRef
Baraniuk, R.G.,
Candes, E.,
Elad, M.,
Ma, Y.,
Applications of Sparse Representation and Compressive Sensing,
PIEEE(98), No. 6, June 2010, pp. 906-909.
IEEE DOI
1006
BibRef
Baraniuk, R.G.[Richard G.],
Cevher, V.,
Wakin, M.B.[Michael B.],
Low-Dimensional Models for Dimensionality Reduction and Signal
Recovery: A Geometric Perspective,
PIEEE(98), No. 6, June 2010, pp. 959-971.
IEEE DOI
1006
BibRef
Duarte, M.F.[Marco F.],
Davenport, M.A.[Mark A.],
Wakin, M.B.[Michael B.],
Laska, J.N.[Jason N.],
Takhar, D.[Dharmpal],
Kelly, K.F.[Kevin F.],
Baraniuk, R.G.[Richard G.],
Multiscale Random Projections for Compressive Classification,
ICIP07(VI: 161-164).
IEEE DOI
0709
BibRef
Wakin, M.B.,
Laska, J.N.,
Duarte, M.F.,
Baron, D.,
Sarvotham, S.,
Takhar, D.,
Kelly, K.F.,
Baraniuk, R.G.,
An Architecture for Compressive Imaging,
ICIP06(1273-1276).
IEEE DOI
0610
BibRef
Duarte, M.F.,
Baraniuk, R.G.,
Kronecker Compressive Sensing,
IP(21), No. 2, February 2012, pp. 494-504.
IEEE DOI
1201
BibRef
Elad, M.,
Figueiredo, M.A.T.,
Ma, Y.,
On the Role of Sparse and Redundant Representations in Image Processing,
PIEEE(98), No. 6, June 2010, pp. 972-982.
IEEE DOI
1006
BibRef
Fadili, M.J.,
Starck, J.L.,
Bobin, J.,
Moudden, Y.,
Image Decomposition and Separation Using Sparse Representations:
An Overview,
PIEEE(98), No. 6, June 2010, pp. 983-994.
IEEE DOI
1006
BibRef
Jacques, L.[Laurent],
Hammond, D.K.[David Kenric],
Fadili, M.J.[M. Jalal],
Weighted fidelity in non-uniformly quantized compressed sensing,
ICIP11(1921-1924).
IEEE DOI
1201
BibRef
Earlier:
De-Quantizing Compressed Sensing with non-Gaussian constraints,
ICIP09(1465-1468).
IEEE DOI
0911
BibRef
Bajwa, W.U.,
Haupt, J.,
Sayeed, A.M.,
Nowak, R.,
Compressed Channel Sensing:
A New Approach to Estimating Sparse Multipath Channels,
PIEEE(98), No. 6, June 2010, pp. 1058-1076.
IEEE DOI
1006
BibRef
Yang, A.Y.,
Gastpar, M.,
Bajcsy, R.,
Sastry, S.S.,
Distributed Sensor Perception via Sparse Representation,
PIEEE(98), No. 6, June 2010, pp. 1077-1088.
IEEE DOI
1006
BibRef
Robucci, R.,
Gray, J.D.,
Chiu, L.K.,
Romberg, J.K.,
Hasler, P.,
Compressive Sensing on a CMOS Separable-Transform Image Sensor,
PIEEE(98), No. 6, June 2010, pp. 1089-1101.
IEEE DOI
1006
BibRef
Yu, L.,
Barbot, J.P.,
Zheng, G.,
Sun, H.,
Compressive Sensing With Chaotic Sequence,
SPLetters(17), No. 8, August 2010, pp. 731-734.
IEEE DOI
1007
BibRef
Fannjiang, A.C.[Albert C.],
Strohmer, T.[Thomas],
Yan, P.C.[Peng-Chong],
Compressed Remote Sensing Of Sparse Objects,
SIIMS(3), No. 3, 2010, pp. 595-618.
DOI Link compressed sensing; incoherence; threshold aperture; Rayleigh
resolution; random sensor array
BibRef
1000
Edwards, J.,
Focus on Compressive Sensing,
SPMag(28), No. 2, 2011, pp. 11-13.
IEEE DOI
1103
Special Reports. Brief survey.
BibRef
Ashok, A.[Amit],
Neifeld, M.A.[Mark A.],
Compressive imaging: hybrid measurement basis design,
JOSA-A(28), No. 6, June 2011, pp. 1041-1050.
DOI Link
1101
BibRef
Ni, K.Y.[Kang-Yu],
Datta, S.[Somantika],
Mahanti, P.[Prasun],
Roudenko, S.[Svetlana],
Cochran, D.[Douglas],
Efficient Deterministic Compressed Sensing for Images with Chirps and
Reed-Muller Codes,
SIIMS(4), No. 3, 2011, pp. 931-953.
WWW Link.
1110
BibRef
Li, B.[Bo],
Shen, Y.[Yi],
Li, J.[Jia],
Dictionaries Construction Using Alternating Projection Method in
Compressive Sensing,
SPLetters(18), No. 11, November 2011, pp. 663-666.
IEEE DOI
1112
BibRef
Chen, W.,
Rodrigues, M.R.D.,
Wassell, I.J.,
On the Use of Unit-Norm Tight Frames to Improve the Average MSE
Performance in Compressive Sensing Applications,
SPLetters(19), No. 1, January 2012, pp. 8-11.
IEEE DOI
1112
BibRef
Chen, W.[Wei],
Wassell, I.J.,
Rodrigues, M.R.D.,
Dictionary Design for Distributed Compressive Sensing,
SPLetters(22), No. 1, January 2015, pp. 95-99.
IEEE DOI
1410
compressed sensing
BibRef
Abou Saleh, A.,
Chan, W.Y.,
Alajaji, F.,
Compressed Sensing With Nonlinear Analog Mapping in a Noisy Environment,
SPLetters(19), No. 1, January 2012, pp. 39-42.
IEEE DOI
1112
BibRef
Yang, Z.,
Zhang, C.,
Xie, L.,
On Phase Transition of Compressed Sensing in the Complex Domain,
SPLetters(19), No. 1, January 2012, pp. 47-50.
IEEE DOI
1112
BibRef
Wu, X.,
Dong, W.,
Zhang, X.,
Shi, G.,
Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging
Applications,
IP(21), No. 2, February 2012, pp. 451-458.
IEEE DOI
1201
BibRef
Hong, S.[Seokbeom],
Park, H.[Hosung],
Shin, B.[Beomkyu],
No, J.S.[Jong-Seon],
Chung, H.[Habong],
A New Performance Measure Using k -Set Correlation for Compressed
Sensing Matrices,
SPLetters(19), No. 3, March 2012, pp. 143-146.
IEEE DOI
1202
BibRef
Fannjiang, A.[Albert],
Liao, W.J.[Wen-Jing],
Coherence Pattern-Guided Compressive Sensing with Unresolved Grids,
SIIMS(5), No. 1 2012, pp. 179.
DOI Link
1203
BibRef
Fu, C.J.[Chang-Jun],
Ji, X.Y.[Xiang-Yang],
Dai, Q.H.[Qiong-Hai],
Adaptive Compressed Sensing Recovery Utilizing the Property of Signal's
Autocorrelations,
IP(21), No. 5, May 2012, pp. 2369-2378.
IEEE DOI
1204
BibRef
Deng, C.[Chao],
Zhang, Y.L.[Yuan-Long],
Mao, Y.F.[Yi-Feng],
Fan, J.T.[Jing-Tao],
Suo, J.L.[Jin-Li],
Zhang, Z.L.[Zhi-Li],
Dai, Q.H.[Qiong-Hai],
Sinusoidal Sampling Enhanced Compressive Camera for High Speed
Imaging,
PAMI(43), No. 4, April 2021, pp. 1380-1393.
IEEE DOI
2103
Encoding, Image reconstruction, Cameras, Image coding,
Frequency modulation, Frequency-domain analysis,
high-speed video
BibRef
Wang, L.,
Wu, X.,
Shi, G.,
Binned Progressive Quantization for Compressive Sensing,
IP(21), No. 6, June 2012, pp. 2980-2990.
IEEE DOI
1202
BibRef
Zou, J.,
Fu, Y.,
Xie, S.,
A Block Fixed Point Continuation Algorithm for Block-Sparse
Reconstruction,
SPLetters(19), No. 6, June 2012, pp. 364-367.
IEEE DOI
1202
BibRef
Xiao, Y.H.[Yun-Hai],
Song, H.N.[Hui-Na],
An Inexact Alternating Directions Algorithm for Constrained Total
Variation Regularized Compressive Sensing Problems,
JMIV(44), No. 2, October 2012, pp. 114-127.
WWW Link.
1206
BibRef
Guo, W.H.[Wei-Hong],
Yin, W.T.[Wo-Tao],
Edge Guided Reconstruction for Compressive Imaging,
SIIMS(5), No. 3 2012, pp. 809-834.
DOI Link
1208
BibRef
Sidiropoulos, N.D.,
Kyrillidis, A.,
Multi-Way Compressed Sensing for Sparse Low-Rank Tensors,
SPLetters(19), No. 11, November 2012, pp. 757-760.
IEEE DOI
1210
BibRef
Bourquard, A.,
Unser, M.,
Binary Compressed Imaging,
IP(22), No. 3, March 2013, pp. 1042-1055.
IEEE DOI
1302
BibRef
Zhang, X.[Xue],
Wang, A.H.[An-Hong],
Zeng, B.[Bing],
Liu, L.[Lei],
Liu, Z.[Zhuo],
Adaptive Block-Wise Compressive Image Sensing Based on Visual
Perception,
IEICE(E96-D), No. 2, February 2013, pp. 383-386.
WWW Link.
1301
BibRef
Lee, H.K.[Hyung-Keuk],
Oh, H.[Heeseok],
Lee, S.H.[Sang-Hoon],
Bovik, A.C.,
Visually Weighted Compressive Sensing: Measurement and Reconstruction,
IP(22), No. 4, April 2013, pp. 1444-1455.
IEEE DOI
1303
BibRef
Earlier: A1, A2, A3, Only:
A new block compressive sensing to control the number of measurements,
ICIP11(2713-2716).
IEEE DOI
1201
BibRef
Zhang, X.Y.[Xiao-Ya],
Li, S.[Song],
Compressed Sensing via Dual Frame Based L_1-Analysis With
Weibull Matrices,
SPLetters(20), No. 3, March 2013, pp. 265-268.
IEEE DOI
1303
BibRef
Liu, Y.,
Li, M.,
Pados, D.A.,
Motion-Aware Decoding of Compressed-Sensed Video,
CirSysVideo(23), No. 3, March 2013, pp. 438-444.
IEEE DOI
1303
BibRef
Liu, Y.,
Pados, D.A.,
Compressed-Sensed-Domain L1-PCA Video Surveillance,
MultMed(18), No. 3, March 2016, pp. 351-363.
IEEE DOI
1603
Computational complexity
BibRef
Yu, X.,
Baek, S.J.,
Sufficient Conditions on Stable Recovery of Sparse Signals With Partial
Support Information,
SPLetters(20), No. 5, May 2013, pp. 539-542.
IEEE DOI
1304
BibRef
Patel, V.M.[Vishal M.],
Chellappa, R.[Rama],
Sparse Representations and Compressive Sensing for Imaging and Vision,
Springer2013.
ISBN 978-1-4614-6380-1.
Yang, Z.L.[Zhi-Li],
Jacob, M.,
Nonlocal Regularization of Inverse Problems:
A Unified Variational Framework,
IP(22), No. 8, 2013, pp. 3192-3203.
IEEE DOI
1307
concave programming; compressive sensing; current schemes;
robust distance metrics; Noise reduction; nonconvex; nonlocal means
BibRef
Sun, B.[Biao],
Chen, Q.[Qian],
Xu, X.[Xinxin],
He, Y.[Yun],
Jiang, J.J.[Jian-Jun],
Permuted and Filtered Spectrum Compressive Sensing,
SPLetters(20), No. 7, 2013, pp. 685-688.
IEEE DOI OFDM modulation; Fourier coefficient; OFDM
1307
BibRef
Du, X.P.[Xin-Peng],
Cheng, L.Z.[Li-Zhi],
Liu, L.F.[Lu-Feng],
A Swarm Intelligence Algorithm for Joint Sparse Recovery,
SPLetters(20), No. 6, 2013, pp. 611-614.
IEEE DOI
1307
Gaussian processes; compressed sensing theory
BibRef
Wu, X.F.[Xiao-Fu],
Yang, Z.[Zhen],
Verification-Based Interval-Passing Algorithm for Compressed Sensing,
SPLetters(20), No. 10, 2013, pp. 933-936.
IEEE DOI
1309
iterative methods
BibRef
Yu, N.Y.,
Zhao, N.,
Deterministic Construction of Real-Valued Ternary Sensing Matrices
Using Optical Orthogonal Codes,
SPLetters(20), No. 11, 2013, pp. 1106-1109.
IEEE DOI
1310
Coherence
BibRef
Shen, Y.,
Fang, J.,
Li, H.,
Exact Reconstruction Analysis of Log-Sum Minimization for Compressed
Sensing,
SPLetters(20), No. 12, 2013, pp. 1223-1226.
IEEE DOI
1311
Compressed sensing
BibRef
Krahmer, F.,
Ward, R.,
Stable and Robust Sampling Strategies for Compressive Imaging,
IP(23), No. 2, February 2014, pp. 612-622.
IEEE DOI
1402
Fourier transforms
BibRef
Huang, T.Y.[Tian-Yao],
Liu, Y.M.[Yi-Min],
Meng, H.D.[Hua-Dong],
Wang, X.[Xiqin],
Adaptive Compressed Sensing via Minimizing Cramer-Rao Bound,
SPLetters(21), No. 3, March 2014, pp. 270-274.
IEEE DOI
1403
adaptive signal processing
BibRef
Foucart, S.,
Koslicki, D.,
Sparse Recovery by Means of Nonnegative Least Squares,
SPLetters(21), No. 4, April 2014, pp. 498-502.
IEEE DOI
1403
Compressed sensing
BibRef
Foucart, S.,
Lecué, G.,
An IHT Algorithm for Sparse Recovery From Subexponential Measurements,
SPLetters(24), No. 9, September 2017, pp. 1280-1283.
IEEE DOI
1708
compressed sensing,
minimisation, probability, IHT algorithm,
classical restricted isometry property,
independent subexponential random variables,
L1-minimization, matrix, probability,
subexponential measurements, uniform sparse recovery,
Compressive sensing,
restricted isometry property, sparse recovery, subexponential
random variable
BibRef
Mohades, M.M.,
Mohades, A.,
Tadaion, A.,
A Reed-Solomon Code Based Measurement Matrix with Small Coherence,
SPLetters(21), No. 7, July 2014, pp. 839-843.
IEEE DOI
1405
Coherence
BibRef
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Li, X.[Xin],
Ma, Y.[Yi],
Huang, F.[Feng],
Compressive Sensing via Nonlocal Low-Rank Regularization,
IP(23), No. 8, August 2014, pp. 3618-3632.
IEEE DOI
1408
biomedical MRI
BibRef
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Li, X.[Xin],
Peng, K.,
Wu, J.,
Guo, Z.,
Color-Guided Depth Recovery via Joint Local Structural and Nonlocal
Low-Rank Regularization,
MultMed(19), No. 2, February 2017, pp. 293-301.
IEEE DOI
1702
computational geometry
BibRef
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Hu, X.C.[Xiao-Cheng],
Ma, Y.[Yi],
Nonlocal Sparse and Low-Rank Regularization for Optical Flow
Estimation,
IP(23), No. 10, October 2014, pp. 4527-4538.
IEEE DOI
1410
image sequences
BibRef
Feng, J.M.[Joe-Mei],
Krahmer, F.,
An RIP-Based Approach to Sigma-Delta Quantization for Compressed
Sensing,
SPLetters(21), No. 11, November 2014, pp. 1351-1355.
IEEE DOI
1408
compressed sensing
BibRef
Li, Y.[Yun],
Sankaranarayanan, A.C.[Aswin C.],
Xu, L.[Lina],
Baraniuk, R.[Richard],
Kelly, K.F.[Kevin F.],
Realization of hybrid compressive imaging strategies,
JOSA-A(31), No. 8, August 2014, pp. 1716-1720.
DOI Link
1408
Inverse problems; Computational imaging
BibRef
Friedland, S.,
Li, Q.[Qun],
Schonfeld, D.,
Compressive Sensing of Sparse Tensors,
IP(23), No. 10, October 2014, pp. 4438-4447.
IEEE DOI
1410
compressed sensing
Compare with Kronecker compressive sensing and
multiway compressive sensing. KC is better compression, this is faster.
BibRef
Lu, Z.Q.[Zhen-Qi],
Ying, R.D.[Ren-Dong],
Jiang, S.X.[Sum-Xin],
Liu, P.L.[Pei-Lin],
Yu, W.X.[Wen-Xian],
Distributed Compressed Sensing off the Grid,
SPLetters(22), No. 1, January 2015, pp. 105-109.
IEEE DOI
1410
compressed sensing
BibRef
Rousseau, S.,
Helbert, D.,
Carre, P.,
Blanc-Talon, J.,
Compressive Pattern Matching on Multispectral Data,
GeoRS(52), No. 12, December 2014, pp. 7581-7592.
IEEE DOI
1410
compressed sensing
BibRef
Ma, J.J.[Jun-Jie],
Yuan, X.J.[Xiao-Jun],
Ping, L.[Li],
Turbo Compressed Sensing with Partial DFT Sensing Matrix,
SPLetters(22), No. 2, February 2015, pp. 158-161.
IEEE DOI
1410
compressed sensing
BibRef
Ma, J.J.[Jun-Jie],
Yuan, X.J.[Xiao-Jun],
Ping, L.[Li],
On the Performance of Turbo Signal Recovery with Partial DFT Sensing
Matrices,
SPLetters(22), No. 10, October 2015, pp. 1580-1584.
IEEE DOI
1506
compressed sensing
BibRef
Cloninger, A.,
Czaja, W.,
Bai, R.,
Basser, P.,
Solving 2D Fredholm Integral from Incomplete Measurements Using
Compressive Sensing,
SIIMS(7), No. 3, 2014, pp. 1775-1798.
DOI Link
1410
BibRef
Liu, H.X.[Hai-Xiao],
Song, B.[Bin],
Tian, F.[Fang],
Qin, H.[Hao],
Joint Sampling Rate and Bit-Depth Optimization in Compressive Video
Sampling,
MultMed(16), No. 6, October 2014, pp. 1549-1562.
IEEE DOI
1410
compressed sensing
BibRef
Wu, Q.S.[Qi-Song],
Zhang, Y.D.,
Amin, M.G.,
Himed, B.,
Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task
Dependency,
SPLetters(22), No. 4, April 2015, pp. 430-434.
IEEE DOI
1411
Bayes methods
BibRef
Zhou, Z.,
Liu, K.,
Fang, J.,
Bayesian Compressive Sensing Using Normal Product Priors,
SPLetters(22), No. 5, May 2015, pp. 583-587.
IEEE DOI
1411
Approximation methods
BibRef
Poli, L.[Lorenzo],
Oliveri, G.[Giacomo],
Ding, P.P.[Ping Ping],
Moriyama, T.[Toshifumi],
Massa, A.[Andrea],
Multifrequency Bayesian compressive sensing methods for microwave
imaging,
JOSA-A(31), No. 11, November 2014, pp. 2415-2428.
DOI Link
1411
Inverse problems
BibRef
Wei, K.,
Fast Iterative Hard Thresholding for Compressed Sensing,
SPLetters(22), No. 5, May 2015, pp. 593-597.
IEEE DOI
1411
Approximation algorithms
BibRef
Zhang, J.C.[Jing-Chao],
Fu, N.[Ning],
Peng, X.Y.[Xi-Yuan],
Compressive Circulant Matrix Based Analog to Information Conversion,
SPLetters(21), No. 4, April 2014, pp. 428-431.
IEEE DOI
1403
compressed sensing
BibRef
Nichols, J.M.,
Oh, A.K.,
Willett, R.M.,
Reducing Basis Mismatch in Harmonic Signal Recovery via Alternating
Convex Search,
SPLetters(21), No. 8, August 2014, pp. 1007-1011.
IEEE DOI
1406
Compressed sensing
BibRef
Chen, Z.,
Molina, R.,
Katsaggelos, A.K.,
Automated Recovery of Compressedly Observed Sparse Signals From
Smooth Background,
SPLetters(21), No. 8, August 2014, pp. 1012-1016.
IEEE DOI
1406
Algorithm design and analysis
BibRef
Goertz, N.,
Guo, C.,
Jung, A.,
Davies, M.E.,
Doblinger, G.,
Iterative Recovery of Dense Signals from Incomplete Measurements,
SPLetters(21), No. 9, September 2014, pp. 1059-1063.
IEEE DOI
1406
Compressed sensing
BibRef
Li, F.[Fuwei],
Fang, J.[Jun],
Li, H.B.[Hong-Bin],
Huang, L.[Lei],
Robust One-Bit Bayesian Compressed Sensing with Sign-Flip Errors,
SPLetters(22), No. 7, July 2015, pp. 857-861.
IEEE DOI
1412
Bayes methods
BibRef
Lu, W.Z.[Wei-Zhi],
Li, W.Y.[Wei-Yu],
Kpalma, K.,
Ronsin, J.,
Compressed Sensing Performance of Random Bernoulli Matrices with High
Compression Ratio,
SPLetters(22), No. 8, August 2015, pp. 1074-1078.
IEEE DOI
1502
compressed sensing
BibRef
Martin, G.,
Bioucas-Dias, J.M.,
Plaza, A.,
HYCA: A New Technique for Hyperspectral Compressive Sensing,
GeoRS(53), No. 5, May 2015, pp. 2819-2831.
IEEE DOI
1502
compressed sensing
BibRef
Xu, S.C.[Song-Cen],
de Lamare, R.C.,
Poor, H.V.,
Distributed Compressed Estimation Based on Compressive Sensing,
SPLetters(22), No. 9, September 2015, pp. 1311-1315.
IEEE DOI
1503
compressed sensing
BibRef
Miller, T.G.,
Xu, S.,
de Lamare, R.C.,
Poor, H.V.,
Distributed Spectrum Estimation Based on Alternating Mixed
Discrete-Continuous Adaptation,
SPLetters(23), No. 4, April 2016, pp. 551-555.
IEEE DOI
1604
Cost function
BibRef
Jang, W.Y.[Woo-Yong],
Ku, Z.[Zahyun],
Urbas, A.,
Derov, J.,
Noyola, M.J.,
Plasmonic Superpixel Sensor for Compressive Spectral Sensing,
GeoRS(53), No. 6, June 2015, pp. 3471-3480.
IEEE DOI
1503
feature extraction
BibRef
Schaeffer, H.[Hayden],
Yang, Y.[Yi],
Osher, S.J.[Stanley J.],
Space-Time Regularization for Video Decompression,
SIIMS(8), No. 1, 2015, pp. 373-402.
DOI Link
1503
From compressive sensing method.
BibRef
Han, H.[Hong],
Gan, L.[Lu],
Liu, S.J.[San-Jun],
Guo, Y.Y.[Yu-Yan],
A Novel Measurement Matrix Based on Regression Model for Block
Compressed Sensing,
JMIV(51), No. 1, January 2015, pp. 161-170.
Springer DOI
1503
BibRef
Guo, J.[Jie],
Song, B.[Bin],
Tian, F.[Fang],
Liu, H.X.[Hai-Xiao],
Qin, H.[Hao],
Perception of Image Characteristics with Compressive Measurements,
IEICE(E97-D), No. 12, December 2014, pp. 3234-3235.
WWW Link.
1503
BibRef
Liu, F.,
Lin, L.,
Jiao, L.,
Li, L.,
Yang, S.,
Hou, B.,
Ma, H.,
Yang, L.,
Xu, J.,
Nonconvex Compressed Sensing by Nature-Inspired Optimization
Algorithms,
Cyber(45), No. 5, May 2015, pp. 1028-1039.
IEEE DOI
1505
Algorithm design and analysis
BibRef
Liu, J.X.[Ji-Xin],
Li, X.F.[Xiao-Fei],
Han, G.[Guang],
Sun, N.[Ning],
Du, K.[Kun],
Sun, Q.S.[Quan-Sen],
Colour compressed sensing imaging via sparse difference and fractal
minimisation recovery,
IET-IPR(9), No. 5, 2015, pp. 369-380.
DOI Link
1506
compressed sensing
BibRef
Nagahara, M.,
Discrete Signal Reconstruction by Sum of Absolute Values,
SPLetters(22), No. 10, October 2015, pp. 1575-1579.
IEEE DOI
1506
compressed sensing
BibRef
Saleh, A.A.[A. Abou],
Alajaji, F.,
Chan, W.Y.[Wai-Yip],
Compressed Sensing with Non-Gaussian Noise and Partial Support
Information,
SPLetters(22), No. 10, October 2015, pp. 1703-1707.
IEEE DOI
1506
Gaussian noise
BibRef
Bi, D.J.[Dong-Jie],
Xie, Y.L.[Yong-Le],
Li, X.F.[Xi-Feng],
Zheng, Y.R.,
A Sparsity Basis Selection Method for Compressed Sensing,
SPLetters(22), No. 10, October 2015, pp. 1738-1742.
IEEE DOI
1506
compressed sensing
BibRef
Cambareri, V.,
Mangia, M.,
Pareschi, F.,
Rovatti, R.,
Setti, G.,
A Case Study in Low-Complexity ECG Signal Encoding:
How Compressing is Compressed Sensing?,
SPLetters(22), No. 10, October 2015, pp. 1743-1747.
IEEE DOI
1506
compressed sensing
BibRef
Zhu, S.Y.[Shu-Yuan],
Zeng, B.[Bing],
Gabbouj, M.[Moncef],
Adaptive sampling for compressed sensing based image compression,
JVCIR(30), No. 1, 2015, pp. 94-105.
Elsevier DOI
1507
Sparsity
BibRef
Zhu, S.Y.[Shu-Yuan],
Zeng, B.[Bing],
Fang, L.[Lu],
Gabbouj, M.[Moncef],
Downward spatially-scalable image reconstruction based on compressed
sensing,
ICIP14(1352-1356)
IEEE DOI
1502
Compressed sensing
BibRef
Qiao, H.[Heng],
Pal, P.,
Generalized Nested Sampling for Compressing Low Rank Toeplitz
Matrices,
SPLetters(22), No. 11, November 2015, pp. 1844-1848.
IEEE DOI
1509
Toeplitz matrices
BibRef
Biswas, S.,
Achanta, H.K.,
Jacob, M.,
Dasgupta, S.,
Mudumbai, R.,
Recovery of Low Rank and Jointly Sparse Matrices with Two Sampling
Matrices,
SPLetters(22), No. 11, November 2015, pp. 1945-1949.
IEEE DOI
1509
compressed sensing
BibRef
Zhang, J.[Jun],
Han, G.J.[Guo-Jun],
Fang, Y.[Yi],
Deterministic Construction of Compressed Sensing Matrices from
Protograph LDPC Codes,
SPLetters(22), No. 11, November 2015, pp. 1960-1964.
IEEE DOI
1509
binary codes
BibRef
Wang, L.[Luhua],
Zhang, J.[Jun],
BPDQ_p-Net: A Deep Unfolding Method for Quantized Compressed Sensing,
SPLetters(31), 2024, pp. 531-535.
IEEE DOI
2402
Quantization (signal), Distortion, Signal reconstruction,
Signal processing algorithms, Compressed sensing, Transforms,
quantized measurements
BibRef
Wang, X.[Xing],
Liang, J.[Jie],
Approximate message passing-based compressed sensing reconstruction
with generalized elastic net prior,
SP:IC(37), No. 1, 2015, pp. 19-33.
Elsevier DOI
1509
BibRef
And:
Multi-resolution compressed sensing reconstruction via approximate
message passing,
ICIP15(4352-4356)
IEEE DOI
1512
Compressed sensing
BibRef
Zhang, Y.L.[Yi-Long],
Li, Y.H.[Yue-Hua],
He, G.H.[Guan-Hua],
Zhang, S.[Sheng],
A Compressive Regularization Imaging Algorithm for Millimeter-Wave SAIR,
IEICE(E98-D), No. 8, August 2015, pp. 1609-1612.
WWW Link.
1509
BibRef
Zhang, Y.L.[Yi-Long],
Li, Y.[Yuehua],
Safavi-Naeini, S.[Safieddin],
A Spectrum-Based Saliency Detection Algorithm for Millimeter-Wave InSAR
Imaging with Sparse Sensing,
IEICE(E100-D), No. 1, February 2017, pp. 388-391.
WWW Link.
1702
BibRef
Warnell, G.,
Bhattacharya, S.,
Chellappa, R.,
Basar, T.,
Adaptive-Rate Compressive Sensing Using Side Information,
IP(24), No. 11, November 2015, pp. 3846-3857.
IEEE DOI
1509
adaptive signal processing
BibRef
Li, S.J.[Shuang-Jiang],
Qi, H.R.[Hai-Rong],
A Douglas-Rachford Splitting Approach to Compressed Sensing Image
Recovery Using Low-Rank Regularization,
IP(24), No. 11, November 2015, pp. 4240-4249.
IEEE DOI
1509
compressed sensing
BibRef
Sankaranarayanan, A.C.[Aswin C.],
Xu, L.[Lina],
Studer, C.[Christoph],
Li, Y.[Yun],
Kelly, K.F.[Kevin F.],
Baraniuk, R.G.[Richard G.],
Video Compressive Sensing for Spatial Multiplexing Cameras Using
Motion-Flow Models,
SIIMS(8), No. 3, 2015, pp. 1489-1518.
DOI Link
1511
BibRef
Earlier: A1, A3, A6, Only:
CS-MUVI: Video compressive sensing for spatial-multiplexing cameras,
ICCP12(1-10).
IEEE DOI
1208
BibRef
Holloway, J.,
Sankaranarayanan, A.C.,
Veeraraghavan, A.,
Tambe, S.,
Flutter Shutter Video Camera for compressive sensing of videos,
ICCP12(1-9).
IEEE DOI
1208
BibRef
Yuan, H.,
Song, H.,
Sun, X.,
Guo, K.,
Ju, Z.,
Compressive sensing measurement matrix construction based on improved
size compatible array LDPC code,
IET-IPR(9), No. 11, 2015, pp. 993-1001.
DOI Link
1511
compressed sensing
BibRef
Sudhakar, P.[Prasad],
Jacques, L.[Laurent],
Dubois, X.[Xavier],
Antoine, P.[Philippe],
Joannes, L.[Luc],
Compressive Imaging and Characterization of Sparse Light Deflection
Maps,
SIIMS(8), No. 3, 2015, pp. 1824-1856.
DOI Link
1511
BibRef
Nicodème, M.[Marc],
Turcu, F.[Flavius],
Dossal, C.[Charles],
Optimal Dual Certificates for Noise Robustness Bounds in Compressive
Sensing,
JMIV(53), No. 3, November 2015, pp. 251-263.
WWW Link.
1511
BibRef
Li, G.[Gang],
Li, X.[Xiao],
Li, S.[Sheng],
Bai, H.[Huang],
Jiang, Q.[Qianru],
He, X.X.[Xiong-Xiong],
Designing robust sensing matrix for image compression,
IP(24), No. 12, December 2015, pp. 5389-5400.
IEEE DOI
1512
compressed sensing
BibRef
Chang, K.[Kan],
Li, B.X.[Bao-Xin],
Joint modeling and reconstruction of a compressively-sensed set of
correlated images,
JVCIR(33), No. 1, 2015, pp. 286-300.
Elsevier DOI
1512
Compressive sensing
BibRef
Romero, D.,
Ariananda, D.,
Tian, Z.,
Leus, G.,
Compressive Covariance Sensing:
Structure-based compressive sensing beyond sparsity,
SPMag(33), No. 1, January 2016, pp. 78-93.
IEEE DOI
1601
BibRef
Lin, L.P.[Le-Ping],
Liu, F.[Fang],
Jiao, L.C.[Li-Cheng],
Geometric structure guided collaborative compressed sensing,
SP:IC(40), No. 1, 2016, pp. 16-25.
Elsevier DOI
1601
Geometric structure
BibRef
Wang, Y.G.[Ying-Gui],
Liu, Z.[Zheng],
Yang, L.[Le],
Jiang, W.L.[Wen-Li],
Generalized compressive detection of stochastic signals using
Neyman-Pearson theorem,
SIViP(9), No. 1 Supp, December 2015, pp. 111-120.
Springer DOI
1601
BibRef
Sadeghigol, Z.[Zahra],
Kahaei, M.H.[Mohammad Hossein],
Haddadi, F.[Farzan],
Model based variational Bayesian compressive sensing using heavy
tailed sparse prior,
SP:IC(41), No. 1, 2016, pp. 158-167.
Elsevier DOI
1602
Bayesian compressive sensing.
based on generalized double Pareto.
BibRef
Chang, K.,
Ding, P.L.K.[P. L. Kevin],
Li, B.,
Compressive Sensing Reconstruction of Correlated Images Using Joint
Regularization,
SPLetters(23), No. 4, April 2016, pp. 449-453.
IEEE DOI
1604
Compressed sensing
BibRef
Sugimura, D.[Daisuke],
Tomabechi, M.[Masaru],
Hosaka, T.[Tadaaki],
Hamamoto, T.[Takayuki],
Compressive multi-spectral imaging using self-correlations of images
based on hierarchical joint sparsity models,
MVA(27), No. 4, May 2016, pp. 499-510.
Springer DOI
1605
BibRef
Liu, X.M.[Xian-Ming],
Zhai, D.M.[De-Ming],
Zhou, J.T.[Jian-Tao],
Zhang, X.F.[Xin-Feng],
Zhao, D.B.[De-Bin],
Gao, W.[Wen],
Compressive Sampling-Based Image Coding for Resource-Deficient Visual
Communication,
IP(25), No. 6, June 2016, pp. 2844-2855.
IEEE DOI
1605
Codecs
BibRef
Eslahi, N.[Nasser],
Aghagolzadeh, A.[Ali],
Compressive Sensing Image Restoration Using Adaptive Curvelet
Thresholding and Nonlocal Sparse Regularization,
IP(25), No. 7, July 2016, pp. 3126-3140.
IEEE DOI
1606
compressed sensing
BibRef
Eslahi, N.,
Foi, A.,
Anisotropic Spatiotemporal Regularization in Compressive Video
Recovery by Adaptively Modeling the Residual Errors as Correlated
Noise,
IVMSP18(1-5)
IEEE DOI
1809
Noise measurement, Spatiotemporal phenomena, Adaptation models,
AWGN, Correlation, Transforms
BibRef
Sasmal, P.,
Naidu, R.R.,
Sastry, C.S.,
Jampana, P.,
Composition of Binary Compressed Sensing Matrices,
SPLetters(23), No. 8, August 2016, pp. 1096-1100.
IEEE DOI
1608
compressed sensing
BibRef
Naidu, R.R.,
Murthy, C.R.,
Construction of Binary Sensing Matrices Using Extremal Set Theory,
SPLetters(24), No. 2, February 2017, pp. 211-215.
IEEE DOI
1702
Gaussian processes
BibRef
Guo, J.,
Song, B.,
Du, X.,
Significance Evaluation of Video Data Over Media Cloud Based on
Compressed Sensing,
MultMed(18), No. 7, July 2016, pp. 1297-1304.
IEEE DOI
1608
cloud computing
BibRef
Liu, S.C.[Sheng-Cai],
Zhang, J.S.[Jiang-She],
Liu, J.M.[Jun-Min],
Yin, Q.Y.[Qing-Yan],
L1/2,1 group sparse regularization for compressive sensing,
SIViP(10), No. 5, May 2016, pp. 861-868.
WWW Link.
1608
BibRef
Ye, J.C.[Jong Chul],
Low-rank Fourier interpolation for compressed sensing imaging,
SPIE(Newsroom), July 27, 2016
DOI Link
1608
A novel annihilating filter-based low-rank Hankel matrix approach can
be combined with classical analytic reconstruction techniques for
application to several compressed sensing imaging problems.
BibRef
Dziwoki, G.,
Averaged Properties of the Residual Error in Sparse Signal
Reconstruction,
SPLetters(23), No. 9, September 2016, pp. 1170-1173.
IEEE DOI
1609
Gaussian processes
BibRef
Zhang, L.Y.[Leo Yu],
Wong, K.W.[Kwok-Wo],
Zhang, Y.S.[Yu-Shu],
Zhou, J.T.[Jian-Tao],
Bi-level Protected Compressive Sampling,
MultMed(18), No. 9, September 2016, pp. 1720-1732.
IEEE DOI
1609
compressed sensing
BibRef
Sankaranarayanan, A.C.,
Herman, M.A.,
Turaga, P.K.,
Kelly, K.F.,
Enhanced Compressive Imaging Using Model-Based Acquisition:
Smarter sampling by incorporating domain knowledge,
SPMag(33), No. 5, September 2016, pp. 81-94.
IEEE DOI
1610
compressed sensing
BibRef
Feng, L.[Lei],
Sun, H.J.[Huai-Jiang],
Sun, Q.S.[Quan-Sen],
Xia, G.Y.[Gui-Yu],
Image compressive sensing via Truncated Schatten-p Norm
regularization,
SP:IC(47), No. 1, 2016, pp. 28-41.
Elsevier DOI
1610
Compressive sensing
BibRef
Choi, J.,
Secure Transmissions via Compressive Sensing in Multicarrier Systems,
SPLetters(23), No. 10, October 2016, pp. 1315-1319.
IEEE DOI
1610
compressed sensing
BibRef
Ahn, J.H.,
Compressive Sensing and Recovery for Binary Images,
IP(25), No. 10, October 2016, pp. 4796-4802.
IEEE DOI
1610
Hadamard matrices
BibRef
Testa, M.[Matteo],
Magli, E.[Enrico],
Compressive Estimation and Imaging Based on Autoregressive Models,
IP(25), No. 11, November 2016, pp. 5077-5087.
IEEE DOI
1610
autoregressive processes
BibRef
Salwa, L.[Lagdali],
Mohammed, R.[Rziza],
Novel phase-based descriptor using bispectrum for texture
classification,
PRL(100), No. 1, 2017, pp. 1-5.
Elsevier DOI
1712
Bispectrum
BibRef
Bai, H.[Huang],
Li, S.[Sheng],
He, X.X.[Xiong-Xiong],
Sensing Matrix Optimization Based on Equiangular Tight Frames With
Consideration of Sparse Representation Error,
MultMed(18), No. 10, October 2016, pp. 2040-2053.
IEEE DOI
1610
compressed sensing
BibRef
Canh, T.N.[Thuong Nguyen],
Dinh, K.Q.[Khanh Quoc],
Jeon, B.W.[Byeung-Woo],
Compressive sensing reconstruction via decomposition,
SP:IC(49), No. 1, 2016, pp. 63-78.
Elsevier DOI
1609
BibRef
Earlier: A2, A1, A3:
Compressive sensing of video with weighted sensing and measurement
allocation,
ICIP15(2065-2069)
IEEE DOI
1512
Compressive sensing.
Compressive sensing of video
BibRef
Chien, T.V.[Trinh Van],
Dinh, K.Q.[Khanh Quoc],
Jeon, B.W.[Byeung-Woo],
Burger, M.[Martin],
Block compressive sensing of image and video with nonlocal Lagrangian
multiplier and patch-based sparse representation,
SP:IC(54), No. 1, 2017, pp. 93-106.
Elsevier DOI
1704
Block compressive sensing
BibRef
Feng, L.[Lei],
Sun, H.J.[Huai-Jiang],
Sun, Q.S.[Quan-Sen],
Xia, G.Y.[Gui-Yu],
Blind compressive sensing using block sparsity and nonlocal low-rank
priors,
JVCIR(42), No. 1, 2017, pp. 37-45.
Elsevier DOI
1701
Blind compressive sensing
BibRef
Choi, J.,
Successive Hypothesis Testing Based Sparse Signal Recovery and Its
Application to MUD in Random Access,
SPLetters(24), No. 2, February 2017, pp. 166-170.
IEEE DOI
1702
compressed sensing
BibRef
Baraniuk, R.G.,
Goldstein, T.,
Sankaranarayanan, A.C.,
Studer, C.,
Veeraraghavan, A.,
Wakin, M.B.,
Compressive Video Sensing:
Algorithms, architectures, and applications,
SPMag(34), No. 1, January 2017, pp. 52-66.
IEEE DOI
1702
Survey, Compressive Sensing. compressed sensing
BibRef
Wang, Y.G.[Ying-Gui],
Yang, L.[Le],
Tang, Z.Y.[Ze-Ying],
Gao, Y.[Yong],
Multitask classification and reconstruction using extended Turbo
approximate message passing,
SIViP(11), No. 2, February 2017, pp. 219-226.
Springer DOI
1702
BibRef
Tu, H.[Hao],
Bu, W.H.[Wei-Hua],
Wang, W.J.[Wen-Jing],
Gao, B.X.[Bing-Xi],
Feng, H.[Hui],
Wu, S.[Shuai],
Applicability of Hadamard relaxation method to MMW and THz Imaging with
compressive sensing,
SIViP(11), No. 3, March 2017, pp. 399-406.
Springer DOI
1702
BibRef
Hu, G.Q.[Gui-Qiang],
Xiao, D.[Di],
Wang, Y.[Yong],
Xiang, T.[Tao],
An image coding scheme using parallel compressive sensing for
simultaneous compression-encryption applications,
JVCIR(44), No. 1, 2017, pp. 116-127.
Elsevier DOI
1703
Compressive sensing
BibRef
Unde, A.S.[Amit Satish],
Deepthi, P.P.,
Block compressive sensing:
Individual and joint reconstruction of correlated images,
JVCIR(44), No. 1, 2017, pp. 187-197.
Elsevier DOI
1703
Compressive sensing
BibRef
Unde, A.S.[Amit Satish],
Deepthi, P.P.,
Rate-distortion analysis of structured sensing matrices for block
compressive sensing of images,
SP:IC(65), 2018, pp. 115-127.
Elsevier DOI
1805
Block compressive sensing, Rate-distortion performance, SRM,
BPBD, Uniform quantization, Entropy coding
BibRef
Xu, J.[Jin],
Zhang, Y.[Yan],
Fu, Z.Z.[Zhi-Zhong],
Zhou, N.[Ning],
Perceptual Distributed Compressive Video Sensing via Reweighted
Sampling and Rate-Distortion Optimized Measurements Allocation,
IEICE(E100-D), No. 4, April 2017, pp. 918-922.
WWW Link.
1704
BibRef
Trigano, T.,
Cohen, J.,
Intensity Estimation of Spectroscopic Signals With an Improved Sparse
Reconstruction Algorithm,
SPLetters(24), No. 5, May 2017, pp. 530-534.
IEEE DOI
1704
compressed sensing
BibRef
Wen, Z.D.[Zai-Dao],
Hou, B.[Biao],
Jiao, L.C.[Li-Cheng],
Joint Sparse Recovery With Semisupervised MUSIC,
SPLetters(24), No. 5, May 2017, pp. 629-633.
IEEE DOI
1704
MUSIC: multiple signal classification.
compressed sensing
BibRef
Song, X.,
Peng, X.,
Xu, J.,
Shi, G.,
Wu, F.,
Distributed Compressive Sensing for Cloud-Based Wireless Image
Transmission,
MultMed(19), No. 6, June 2017, pp. 1351-1364.
IEEE DOI
1705
BibRef
Earlier:
Compressive sensing based image transmission with side information at
the decoder,
VCIP15(1-4)
IEEE DOI
1605
Bandwidth, Correlation, Decoding, Image coding, Scalability,
Signal to noise ratio, Silicon,
Distributed compressive sensing (CS),
graceful degradation (GD), image transmission, side, information, (SI)
BibRef
Song, X.,
Peng, X.,
Xu, J.,
Shi, G.,
Wu, F.,
Unequal Error Protection for Scalable Video Storage in the Cloud,
MultMed(20), No. 3, March 2018, pp. 699-710.
IEEE DOI
1802
Bandwidth, Cloud computing, Maintenance engineering, Redundancy,
Static VAr compensators, Streaming media,
simulcast
BibRef
Gholami, M.,
Alinia, M.,
Explicit APM-LDPC Codes With Girths 6, 8, and 10,
SPLetters(24), No. 6, June 2017, pp. 741-745.
IEEE DOI
1705
matrix algebra, parity check codes, affine permutation matrices,
explicit APM-LDPC codes, exponent matrices,
low-density parity-check codes, novel explicit constructions,
Computers, Decoding, Hardware, Linear codes, Mathematics,
Parity check codes, Simulation, Explicit constructions, girth,
low-density, parity-check, codes, from, affine, permutation, matrices,
(APM-LDPC, codes)
BibRef
Ravazzi, C.,
Coluccia, G.[Giulio],
Magli, E.[Enrico],
Curl-Constrained Gradient Estimation for Image Recovery From Highly
Incomplete Spectral Data,
IP(26), No. 6, June 2017, pp. 2656-2668.
IEEE DOI
1705
data compression, image coding,
l1-minimization methods,
compressed Fourier measurements, compressed sensing problem,
curl-constrained gradient estimation, gradient field,
gradient-based methods, highly incomplete spectral data,
least squares estimation,
spectral coefficients, Estimation, Fourier transforms,
Magnetic resonance imaging, Minimization,
TV, Compressed sensing,
BibRef
Bioglio, V.[Valerio],
Coluccia, G.[Giulio],
Magli, E.[Enrico],
Sparse image recovery using compressed sensing over finite alphabets,
ICIP14(1287-1291)
IEEE DOI
1502
Compressed sensing
BibRef
Dinh, K.Q.[Khanh Quoc],
Shim, H.J.[Hiuk Jae],
Jeon, B.W.[Byeung-Woo],
Small-block sensing and larger-block recovery in block-based
compressive sensing of images,
SP:IC(55), No. 1, 2017, pp. 10-22.
Elsevier DOI
1705
Compressive sensing
BibRef
Dinh, K.Q.[Khanh Quoc],
Jeon, B.W.[Byeung-Woo],
Iterative Weighted Recovery for Block-Based Compressive Sensing of
Image/Video at a Low Subrate,
CirSysVideo(27), No. 11, November 2017, pp. 2294-2308.
IEEE DOI
1712
Algorithm design and analysis, Compressed sensing,
Discrete cosine transforms, Image coding, Sensors, Videos,
prior information
BibRef
Geng, T.,
Sun, G.,
Xu, Y.,
He, J.,
Truncated Nuclear Norm Minimization Based Group Sparse Representation
for Image Restoration,
SIIMS(11), No. 3, 2018, pp. 1878-1897.
DOI Link
1810
BibRef
Geng, T.,
Sun, G.,
Xu, Y.,
Li, Z.,
Image compressive sensing using group sparse representation via
truncated nuclear norm minimization,
WSSIP17(1-5)
IEEE DOI
1707
Compressed sensing, Convergence, Convex functions, Dictionaries,
Image processing, Minimization, Sparse matrices,
Compressive sensing, dictionary learning,
group sparse representation, truncated, nuclear, norm, minimization
BibRef
Yan, B.[Bai],
Zhao, Q.[Qi],
Wang, Z.H.[Zhi-Hai],
Zhao, X.Y.[Xin-Yuan],
A hybrid evolutionary algorithm for multiobjective sparse
reconstruction,
SIViP(11), No. 6, September 2017, pp. 993-1000.
Springer DOI
1708
BibRef
Wang, L.Z.[Li-Zhi],
Xiong, Z.W.[Zhi-Wei],
Shi, G.M.[Guang-Ming],
Wu, F.[Feng],
Zeng, W.J.[Wen-Jun],
Adaptive Nonlocal Sparse Representation for Dual-Camera Compressive
Hyperspectral Imaging,
PAMI(39), No. 10, October 2017, pp. 2104-2111.
IEEE DOI
1709
Hyperspectral imaging,
Compressive sensing, dual-camera, hyperspectral imaging,
nonlocal similarity, sparse, representation
BibRef
Elzanaty, A.[Ahmed],
Giorgetti, A.[Andrea],
Chiani, M.[Marco],
Weak RIC Analysis of Finite Gaussian Matrices for Joint Sparse
Recovery,
SPLetters(24), No. 10, October 2017, pp. 1473-1477.
IEEE DOI
1710
RIC: restricted isometry constant.
compressed sensing,
joint sparse reconstruction algorithms,
BibRef
Rateb, A.M.,
Syed-Yusof, S.K.,
Rashid, R.A.,
On the Impact of Prefiltering on Compressed Sensing in Presence of
Invalid Measurements,
SPLetters(24), No. 12, December 2017, pp. 1886-1890.
IEEE DOI
1712
Compressed sensing, Current measurement, Gain measurement,
Minimization, Noise measurement, Random variables, Sensors,
sparse recovery
BibRef
Testa, M.[Matteo],
Magli, E.[Enrico],
Compressive Bayesian K-SVD,
SP:IC(60), No. 1, 2018, pp. 1-5.
Elsevier DOI
1712
Compressed sensing
BibRef
Zha, Z.Y.[Zhi-Yuan],
Liu, X.[Xin],
Zhang, X.G.[Xing-Gan],
Chen, Y.[Yang],
Tang, L.[Lan],
Bai, Y.C.[Ye-Chao],
Wang, Q.[Qiong],
Shang, Z.H.[Zhen-Hong],
Compressed sensing image reconstruction via adaptive sparse nonlocal
regularization,
VC(34), No. 1, January 2018, pp. 117-137.
Springer DOI
1801
BibRef
Gu, X.Y.[Xiao-Yi],
Tu, S.Y.[Shenyin-Ying],
Shi, H.J.M.[Hao-Jun Michael],
Case, M.[Mindy],
Needell, D.[Deanna],
Plan, Y.[Yaniv],
Optimizing Quantization for Lasso Recovery,
SPLetters(25), No. 1, January 2018, pp. 45-49.
IEEE DOI
1801
Quantized compressed sensing,
assuming that Lasso is used for signal estimation.
compressed sensing, optimisation, quantisation (signal),
Lasso recovery, constrained Lloyd-Max-like framework,
quantized compressed sensing (CS)
BibRef
Lu, C.Y.[Can-Yi],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Lin, Z.C.[Zhou-Chen],
A Unified Alternating Direction Method of Multipliers by Majorization
Minimization,
PAMI(40), No. 3, March 2018, pp. 527-541.
IEEE DOI
1802
Compressed sensing, Convergence,
Jacobian matrices, Minimization, Radio frequency, Standards,
mixed ADMM
BibRef
Unde, A.S.[Amit Satish],
Deepthi, P.P.,
Fast BCS-FOCUSS and DBCS-FOCUSS with augmented Lagrangian and minimum
residual methods,
JVCIR(52), 2018, pp. 92-100.
Elsevier DOI
1804
Block compressive sensing FOCal Underdetermined System Solver.
Block compressive sensing, BCS-FOCUSS, DBCS-FOCUSS,
BCS-augmented Lagrangian method, Minimum residual method
BibRef
Mashhadi, M.B.[Mahdi Boloursaz],
Gazor, S.[Saeed],
Rahnavard, N.[Nazanin],
Marvasti, F.[Farokh],
Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by
Predictive Level Comparisons,
SPLetters(25), No. 4, April 2018, pp. 496-500.
IEEE DOI
1804
compressed sensing, error correction, iterative methods,
signal reconstruction, signal sampling, spectral analysis,
sparse signal acquisition
BibRef
Hsieh, S.H.,
Lu, C.S.,
Pei, S.C.,
Compressive Sensing Matrix Design for Fast Encoding and Decoding via
Sparse FFT,
SPLetters(25), No. 4, April 2018, pp. 591-595.
IEEE DOI
1804
Decoding, Dictionaries, Encoding, Frequency-domain analysis,
Hardware, Sensors, Sparse matrices, Compressive sensing (CS),
sparsity
BibRef
Chen, Z.,
Tian, W.W.[Wen-Wen],
Qian, X.,
Gong, C.[Chen],
Efficient and Robust Image Coding and Transmission Based on Scrambled
Block Compressive Sensing,
MultMed(20), No. 7, July 2018, pp. 1610-1621.
IEEE DOI
1806
Complexity theory, Encoding, Image coding, Image reconstruction,
Quantization (signal), Sensors, Visualization,
robust image compression
BibRef
Hou, X.S.[Xing-Song],
Tian, W.W.[Wen-Wen],
Gong, C.[Chen],
Robust and efficient SAR image coding transmission based on
compressive sensing,
ICIP14(2512-2516)
IEEE DOI
1502
Compressed sensing
BibRef
Wang, H.[Huake],
Li, Z.[Ziang],
Hou, X.S.[Xing-Song],
Versatile Denoising-Based Approximate Message Passing for Compressive
Sensing,
IP(32), 2023, pp. 2761-2775.
IEEE DOI
2305
Image reconstruction, Noise reduction, Noise level, Distortion,
Compressed sensing, Task analysis, Smoothing methods,
fine-grained noise level division
BibRef
Zhu, Z.,
Li, G.,
Ding, J.,
Li, Q.,
He, X.,
On Collaborative Compressive Sensing Systems:
The Framework, Design, and Algorithm,
SIIMS(11), No. 2, 2018, pp. 1717-1758.
DOI Link
1807
BibRef
He, F.,
Huang, X.,
Liu, Y.,
Yan, M.,
Fast Signal Recovery From Saturated Measurements by Linear Loss and
Nonconvex Penalties,
SPLetters(25), No. 9, September 2018, pp. 1374-1378.
IEEE DOI
1809
compressed sensing, concave programming, losses,
minimax techniques, linear loss, nonconvex penalties,
saturation
BibRef
Jiang, W.[Wei],
Yang, J.J.[Jun-Jie],
Energy-constraint rate distortion optimization for compressive
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SIViP(12), No. 7, October 2018, pp. 1419-1427.
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BibRef
Wimalajeewa, T.,
Varshney, P.K.,
Compressive Sensing Based Classification in the Presence of Intra-and
Inter-Signal Correlation,
SPLetters(25), No. 9, September 2018, pp. 1398-1402.
IEEE DOI
1809
compressed sensing, signal classification, signal reconstruction,
compression ratio, intra-signal correlation,
correlated data
BibRef
Garcia, H.,
Correa, C.V.,
Arguello, H.,
Multi-Resolution Compressive Spectral Imaging Reconstruction from
Single Pixel Measurements,
IP(27), No. 12, December 2018, pp. 6174-6184.
IEEE DOI
1810
approximation theory, cameras, compressed sensing,
image reconstruction, image resolution, spectral analysis,
compressive spectral imaging
BibRef
Garcia, H.,
Correa, C.V.,
Arguello, H.,
Optimized Sensing Matrix for Single Pixel Multi-Resolution
Compressive Spectral Imaging,
IP(29), 2020, pp. 4243-4253.
IEEE DOI
2002
Sensing matrix design, single pixel camera,
compressive spectral imaging, multi-resolution
BibRef
Ramirez, J.M.,
Arguello, H.,
Spectral Image Classification from Multi-Sensor Compressive
Measurements,
GeoRS(58), No. 1, January 2020, pp. 626-636.
IEEE DOI
2001
Feature extraction, Image coding, Apertures, Optical imaging,
Optical sensors, Compressive spectral imaging (CSI),
spectral image classification
BibRef
Vargas, E.,
Espitia, Ó.,
Arguello, H.,
Tourneret, J.,
Spectral Image Fusion From Compressive Measurements,
IP(28), No. 5, May 2019, pp. 2271-2282.
IEEE DOI
1903
compressed sensing, image fusion, image reconstruction,
image resolution, image sampling, inverse problems,
remote sensing
BibRef
Hinojosa, C.,
Ramirez, J.M.,
Arguello, H.,
Spectral-Spatial Classification from Multi-Sensor Compressive
Measurements Using Superpixels,
ICIP19(3143-3147)
IEEE DOI
1910
compressive spectral imaging, multi-sensor measurements,
spectral image classification, feature extraction, superpixel algorithms.
BibRef
Gan, H.P.[Hong-Ping],
Xiao, S.[Song],
Zhao, Y.M.[Yi-Min],
Xue, X.[Xiao],
Construction of efficient and structural chaotic sensing matrix for
compressive sensing,
SP:IC(68), 2018, pp. 129-137.
Elsevier DOI
1810
Compressive sensing, Structural sensing matrix,
Mutual coherence, Chebyshev chaotic sequence
BibRef
Suwanwimolkul, S.,
Zhang, L.,
Gong, D.,
Zhang, Z.,
Chen, C.,
Ranasinghe, D.C.,
Qinfeng Shi, J.,
An Adaptive Markov Random Field for Structured Compressive Sensing,
IP(28), No. 3, March 2019, pp. 1556-1570.
IEEE DOI
1812
Adaptation models, Probabilistic logic, Compressed sensing,
Estimation, Markov processes, Biomedical measurement,
sparse representation
BibRef
Li, W.[Wan],
Liu, F.[Fang],
Jiao, L.C.[Li-Cheng],
Hu, F.[Fei],
Yang, S.Y.[Shu-Yuan],
Video reconstruction based on Intrinsic Tensor Sparsity model,
SP:IC(72), 2019, pp. 113-125.
Elsevier DOI
1902
Compressive sensing, Gaussian mixture model, Joint sparsity,
Intrinsic Tensor Sparsity, CACTI
BibRef
Li, D.[Dan],
Wu, Z.J.[Zhao-Jun],
Wang, Q.A.[Qi-Ang],
Edge guided compressive sensing for image reconstruction based on
two-stage L_0 minimization,
JVCIR(59), 2019, pp. 461-474.
Elsevier DOI
1903
Compressive sensing, Image reconstruction, minimization,
Edge prior, Multiple sampling scheme
BibRef
Li, F.,
Hong, S.,
Gu, Y.,
Wang, L.,
An Optimization-Oriented Algorithm for Sparse Signal Reconstruction,
SPLetters(26), No. 3, March 2019, pp. 515-519.
IEEE DOI
1903
compressed sensing, computational complexity, greedy algorithms,
optimisation, search problems, signal reconstruction,
optimization-oriented algorithm
BibRef
Daei, S.[Sajad],
Haddadi, F.[Farzan],
Amini, A.[Arash],
Distribution-Aware Block-Sparse Recovery via Convex Optimization,
SPLetters(26), No. 4, April 2019, pp. 528-532.
IEEE DOI
1903
Bayes methods, compressed sensing, convex programming, probability,
signal reconstruction, signal sampling, block-sparse signal,
convex optimization
BibRef
Wang, Z.L.[Ze-Long],
Zhu, J.[Jubo],
Compressive spectral feature sensing,
IET-IPR(13), No. 4, March 2019, pp. 644-652.
DOI Link
1903
BibRef
Wang, Q.[Qian],
Qu, G.R.[Gang-Rong],
Han, G.H.[Guang-Hui],
A thresholding algorithm for sparse recovery via Laplace norm,
SIViP(13), No. 2, March 2019, pp. 389-395.
Springer DOI
1904
Recovery of signal from sparse input.
BibRef
Zhao, R.,
Fu, J.,
Ren, L.,
Wang, Q.,
Strategy for Accelerating Multiway Greedy Compressive Sensing
Reconstruction,
SPLetters(26), No. 5, May 2019, pp. 690-694.
IEEE DOI
1905
compressed sensing, computational complexity, greedy algorithms,
iterative methods, signal reconstruction, iterations,
Tucker decomposition
BibRef
Abedi, M.,
Sun, B.,
Zheng, Z.,
A Sinusoidal-Hyperbolic Family of Transforms With Potential
Applications in Compressive Sensing,
IP(28), No. 7, July 2019, pp. 3571-3583.
IEEE DOI
1906
Image coding, Shape, Vibrations, Transforms, Sensors, Redundancy,
Compressed sensing, Basis, compressive sensing, eigendecomposition,
vibration
BibRef
Rey-Escudero, S.[Samuel],
Garcia, F.J.I.[Fernando Jose Iglesias],
Cabrera, C.[Cristóbal],
Marques, A.G.[Antonio G.],
Sampling and Reconstruction of Diffused Sparse Graph Signals From
Successive Local Aggregations,
SPLetters(26), No. 8, August 2019, pp. 1142-1146.
IEEE DOI
1908
compressed sensing, graph theory, signal reconstruction,
signal sampling, diffused sparse graph signals,
distributed source localization
BibRef
Dolatabadi, H.M.,
Amini, A.,
Deterministic Design of Toeplitz Matrices With Small Coherence Based
on Weyl Sums,
SPLetters(26), No. 10, October 2019, pp. 1501-1505.
IEEE DOI
1909
Coherence, Sparse matrices, Sensors, Compressed sensing,
Tools, Channel estimation, Coherence, compressive sensing,
Weyl sum
BibRef
Keshavarzian, R.[Razieh],
Aghagolzadeh, A.[Ali],
Rezaii, T.Y.[Tohid Yousefi],
LLP norm regularization based group sparse representation for image
compressed sensing recovery,
SP:IC(78), 2019, pp. 477-493.
Elsevier DOI
1909
Compressed sensing, Group sparse representation,
Half-quadratic theory, Image recovery, Nonlocal sparsity
BibRef
Li, H.G.[Hong-Gui],
Compressive domain spatial-temporal difference saliency-based realtime
adaptive measurement method for video recovery,
IET-IPR(13), No. 11, 19 September 2019, pp. 2008-2017.
DOI Link
1909
BibRef
Rousseau, S.[Sylvain],
Helbert, D.[David],
Compressive Color Pattern Detection Using Partial Orthogonal
Circulant Sensing Matrix,
IP(29), No. 1, 2020, pp. 670-678.
IEEE DOI
1910
One key issue in compressive sensing is to design a sensing matrix
that is random enough to have a good signal reconstruction quality.
compressed sensing, edge detection, image colour analysis,
pattern recognition, signal reconstruction,
image color analysis
BibRef
Porta, C.J.D.[C. J. Della],
Bekit, A.A.,
Lampe, B.H.,
Chang, C.,
Hyperspectral Image Classification via Compressive Sensing,
GeoRS(57), No. 10, October 2019, pp. 8290-8303.
IEEE DOI
1910
compressed sensing, geophysical image processing,
geophysical signal processing, hyperspectral imaging,
universality
BibRef
Wu, Z.L.[Zong-Liang],
Yang, C.S.[Cheng-Shuai],
Su, X.F.[Xiong-Fei],
Yuan, X.[Xin],
Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging,
IJCV(131), No. 7, July 2023, pp. 1662-1679.
Springer DOI
2307
BibRef
Wang, P.[Ping],
Wang, L.[Lishun],
Yuan, X.[Xin],
Deep Optics for Video Snapshot Compressive Imaging,
ICCV23(10612-10622)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yuan, X.,
Brady, D.J.,
Katsaggelos, A.K.,
Snapshot Compressive Imaging: Theory, Algorithms, and Applications,
SPMag(38), No. 2, March 2021, pp. 65-88.
IEEE DOI
2103
Signal processing algorithms,
Detectors, Tomography, Signal processing,
Image reconstruction
BibRef
Wang, Z.J.[Zheng-Jue],
Zhang, H.[Hao],
Cheng, Z.H.[Zi-Heng],
Chen, B.[Bo],
Yuan, X.[Xin],
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive
Sensing,
CVPR21(2083-2092)
IEEE DOI
2111
Training, Deep learning, Modulation, Imaging,
Graphics processing units, Detectors, Data models
BibRef
Chen, C.[Can],
Zhou, C.[Chao],
Liu, J.[Jian],
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Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model,
IEICE(E102-D), No. 11, November 2019, pp. 2214-2220.
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1912
Distributed compressive video sensing.
BibRef
Marks, R.J.[Robert J.],
Sampling below the Nyquist density using spectral subtiles,
JOSA-A(36), No. 8, August 2019, pp. 1322-1332.
DOI Link
1912
Composite materials, Fourier transforms, Interpolation,
Multiplexing, Reflection, Spectral imaging
BibRef
Stankovic, L.,
Mandic, D.P.,
Dakovic, M.,
Kisil, I.,
Demystifying the Coherence Index in Compressive Sensing,
SPMag(37), No. 1, January 2020, pp. 152-162.
IEEE DOI
2001
[Lecture Notes]
Sparse matrices, Indexes, Discrete Fourier transforms,
Weight measurement, Signal processing
BibRef
Wang, B.,
Geng, J.,
Efficient Deblending in the PFK Domain Based on Compressive Sensing,
GeoRS(58), No. 2, February 2020, pp. 995-1003.
IEEE DOI
2001
Transforms, Compressed sensing, Data processing, Receivers,
Delay effects, Data acquisition, Curvelet transform, sparsity
BibRef
Trevisi, M.,
Akbari, A.,
Trocan, M.,
Rodríguez-Vázquez, Á.,
Carmona-Galán, R.,
Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and
Landweber Reconstruction,
CirSysVideo(30), No. 2, February 2020, pp. 387-399.
IEEE DOI
2002
Sparse matrices, Image coding, Generators, Flip-flops,
Image reconstruction, Symmetric matrices, Matrix decomposition,
ternary measurement matrix
BibRef
Monsalve, J.,
Rueda-Chacon, H.,
Arguello, H.,
Sensing Matrix Design for Compressive Spectral Imaging via Binary
Principal Component Analysis,
IP(29), 2020, pp. 4003-4012.
IEEE DOI
2002
Compressive spectral imaging,
binary principal component analysis, sensing matrix design
BibRef
Vlašic, T.[Tin],
Ralašic, I.[Ivan],
Tafro, A.[Azra],
Seršic, D.[Damir],
Spline-like Chebyshev polynomial model for compressive imaging,
JVCIR(66), 2020, pp. 102731.
Elsevier DOI
2003
Polynomial representation of image, Chebyshev moments,
Runge phenomenon, Sparse modeling, Compressive sensing, 2D-imaging
BibRef
Chen, J.[Jian],
Chen, Z.F.[Zhi-Feng],
Su, K.X.[Kai-Xiong],
Peng, Z.[Zheng],
Ling, N.[Nam],
Video compressed sensing reconstruction based on structural group
sparsity and successive approximation estimation model,
JVCIR(66), 2020, pp. 102734.
Elsevier DOI
2003
Compressed sensing, Group sparsity, Interframe estimation,
Reconstruction algorithms
BibRef
Feuillen, T.,
Davies, M.E.,
Vandendorpe, L.[Luc],
Jacques, L.[Laurent],
(L_1,L_2)-RIP and Projected Back-Projection Reconstruction for
Phase-Only Measurements,
SPLetters(27), 2020, pp. 396-400.
IEEE DOI
2004
Compressed sensing, restricted isometry property,
complex Gaussian, phase-only
BibRef
Wu, C.[Cathy],
Pozdnukhov, A.[Alexei],
Bayen, A.M.[Alexandre M.],
Block Simplex Signal Recovery:
Methods, Trade-Offs, and an Application to Routing,
ITS(21), No. 4, April 2020, pp. 1547-1559.
IEEE DOI
2004
Estimation, Bayes methods, Compressed sensing, Convex functions,
Sensors, Scalability, Transportation, Compressed sensing,
signal reconstruction
BibRef
Zhang, H.,
Lei, H.,
The Failure Case of Phase Transition for Penalized Problems in
Corrupted Sensing,
SPLetters(27), 2020, pp. 555-559.
IEEE DOI
2005
Sensors, Upper bound, Extraterrestrial measurements,
Geophysical measurements, Geometry, Gaussian processes, Simulation,
compressed sensing
BibRef
Belyaev, E.[Evgeny],
Codreanu, M.[Marian],
Juntti, M.[Markku],
Egiazarian, K.O.[Karen O.],
Compressive sensed video recovery via iterative thresholding with
random transforms,
IET-IPR(14), No. 6, 11 May 2020, pp. 1187-1199.
DOI Link
2005
BibRef
Hezave, H.,
Javadzadeh, M.,
Kahaei, M.H.[Mohammad Hossein],
Sparse Signal Reconstruction Using Blind Super-Resolution With
Arbitrary Sampling,
SPLetters(27), 2020, pp. 615-619.
IEEE DOI
2005
Frequency modulation, Signal resolution, Deconvolution,
Image resolution, Wave functions, Compressed sensing,
joint spectral sparsity
BibRef
Grosche, S.,
Regensky, A.,
Seiler, J.[Jürgen],
Kaup, A.[André],
Boosting Compressed Sensing Using Local Measurements and Sliding
Window Reconstruction,
IP(29), 2020, pp. 7931-7944.
IEEE DOI
2007
Image Reconstruction, local measurements, compressed sensing,
non-regular sampling
BibRef
Canh, T.N.[Thuong Nguyen],
Jeon, B.W.[Byeung-Woo],
Restricted Structural Random Matrix for compressive sensing,
SP:IC(90), 2021, pp. 116017.
Elsevier DOI
2012
Sampling matrix.
Compressive sensing, Structural sparse matrix,
Restricted isometry property, Security, Kronecker compressive sensing
BibRef
Tai, C.L.,
Hsieh, S.H.,
Lu, C.S.,
Greedy Algorithms for Hybrid Compressed Sensing,
SPLetters(27), 2020, pp. 2059-2063.
IEEE DOI
2012
Error bound, greedy algorithms, hybrid compressed sensing (CS)
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Qin, S.[Shun],
Simple algorithm for L1-norm regularisation-based compressed sensing
and image restoration,
IET-IPR(14), No. 14, December 2020, pp. 3405-3413.
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2012
BibRef
Zha, Z.Y.[Zhi-Yuan],
Yuan, X.[Xin],
Zhou, J.T.Y.[Joey Tian-Yi],
Zhou, J.T.[Jian-Tao],
Wen, B.H.[Bi-Han],
Zhu, C.[Ce],
The Power Of Triply Complementary Priors For Image Compressive
Sensing,
ICIP20(983-987)
IEEE DOI
2011
Image restoration, Optimization, Inverse problems, Standards,
Noise reduction, Compressed sensing, Sensors, Image CS,
non-local self-similarity
BibRef
Mishra, D.[Dipti],
Singh, S.K.[Satish Kumar],
Singh, R.K.[Rajat Kumar],
Mishra, D.,
Singh, S.K.,
Singh, R.K.,
Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image
Compression,
CirSysVideo(31), No. 4, April 2021, pp. 1452-1462.
IEEE DOI
2104
Image coding, Wavelet transforms, Decoding, Image resolution,
Convolutional codes, Machine learning, Wavelet, deep, CNN, frequency,
autoencoder
BibRef
Sasmal, P.[Pradip],
Jampana, P.[Phanindra],
Sastry, C.S.[Challa S.],
Construction of Binary Matrices as a Union of Orthogonal Blocks via
Generalized Euler Squares,
SPLetters(28), 2021, pp. 882-886.
IEEE DOI
2106
Germanium, Sparse matrices, Coherence, Indexes,
Matching pursuit algorithms, Image coding, Compressed sensing,
block coherence
BibRef
Torkamani, R.[Razieh],
Zayyani, H.[Hadi],
Sadeghzadeh, R.A.[Ramazan Ali],
Model-based decentralized Bayesian algorithm for distributed
compressed sensing,
SP:IC(95), 2021, pp. 116212.
Elsevier DOI
2106
Distributed compressive sensing, Joint sparsity,
Wavelet-tree structure, Bessel K-form, Variational Bayesian inference
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Das, S.[Samiran],
Hyperspectral image, video compression using sparse Tucker tensor
decomposition,
IET-IPR(15), No. 4, 2021, pp. 964-973.
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Zhang, B.[Bo],
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Robust Coding of Encrypted Images via 2D Compressed Sensing,
MultMed(23), 2021, pp. 2656-2671.
IEEE DOI
2109
Image coding, Encryption, Computational complexity,
Robustness, 2D compressed sensing, image encryption
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Kazemi, V.[Vahdat],
Shahzadi, A.[Ali],
Bizaki, H.K.[Hossein Khaleghi],
New flexible deterministic compressive measurement matrix based on
finite Galois field,
IET-IPR(16), No. 1, 2022, pp. 239-251.
DOI Link
2112
Construct sensing martix.
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Li, J.H.[Jia-Hang],
Zhu, Q.[Qi],
Wu, Y.[Yuezhou],
Gao, X.Y.[Xu-Yang],
Image Reconstruction Based on Deep Iterative Shrinkage Network,
ICIVC21(259-263)
IEEE DOI
2112
Upper bound, Thresholding (Imaging), Stability analysis,
Velocity measurement, Image reconstruction, Optimization,
deep neural network
BibRef
Cai, L.[Lei],
Fu, Y.[Yuli],
Zhu, T.[Tao],
Xiang, Y.J.[You-Jun],
Zeng, H.Q.[Huan-Qiang],
Proximal-Gen for fast compressed sensing recovery,
JVCIR(82), 2022, pp. 103358.
Elsevier DOI
2201
Compressed sensing, Generative models, Generator range,
Reconstruction efficiency
BibRef
Cai, L.[Lei],
Fu, Y.[Yuli],
Xiang, Y.J.[You-Jun],
Zhu, T.,
Li, X.,
Zeng, H.Q.[Huan-Qiang],
Fast compressed sensing recovery using generative models and sparse
deviations modeling,
VCIP20(447-450)
IEEE DOI
2102
Optimization, Image reconstruction, Compressed sensing,
Measurement uncertainty, Computational modeling, Sensors,
projected gradient descent
BibRef
Wan, R.[Rentao],
Zhou, J.J.[Jin-Jia],
Huang, B.[Bowen],
Zeng, H.[Hui],
Fan, Y.[Yibo],
APMC: Adjacent Pixels Based Measurement Coding System for
Compressively Sensed Images,
MultMed(24), 2022, pp. 3558-3569.
IEEE DOI
2207
Image coding, Prediction algorithms, Encoding, Correlation,
Image reconstruction, Current measurement, Compressed sensing,
measurement-domain prediction
BibRef
Lee, B.[Bokyeung],
Ko, K.[Kyungdeuk],
Hong, J.[Jonghwan],
Ku, B.[Bonhwa],
Ko, H.S.[Han-Seok],
Information Bottleneck Measurement for Compressed Sensing Image
Reconstruction,
SPLetters(29), 2022, pp. 1943-1947.
IEEE DOI
2209
Sensors, Generators, Decoding, Training, Image reconstruction,
Image coding, Loss measurement, Information bottleneck,
deep learning
BibRef
Shen, M.[Minghe],
Gan, H.P.[Hong-Ping],
Ning, C.[Chao],
Hua, Y.[Yi],
Zhang, T.[Tao],
TransCS: A Transformer-Based Hybrid Architecture for Image Compressed
Sensing,
IP(31), 2022, pp. 6991-7005.
IEEE DOI
2212
Image reconstruction, Transformers, Sensors, Task analysis,
Compressed sensing, Matching pursuit algorithms, Head,
image reconstruction
BibRef
Heshmati, A.[Alireza],
Amini, S.[Sajjad],
Ghaemmaghami, S.[Shahrokh],
Marvasti, F.[Farokh],
Designing Low Coherent Measurement Matrix With Controlled Spectral
Norm Via an Efficient Approximation of L_inf-Norm,
SPLetters(29), 2022, pp. 2243-2247.
IEEE DOI
2212
Linear programming, Sparse matrices, Matrix decomposition,
Current measurement, Coherence, Sensors, Optimization, Low coherent,
measurement matrix
BibRef
Zhang, J.[Jian],
Chen, B.[Bin],
Xiong, R.Q.[Rui-Qin],
Zhang, Y.B.[Yong-Bing],
Physics-Inspired Compressive Sensing: Beyond deep unrolling,
SPMag(40), No. 1, January 2023, pp. 58-72.
IEEE DOI
2301
Image coding, Computational modeling,
Signal processing algorithms, Transforms, Task analysis
BibRef
Xu, J.[Jin],
Fu, Z.Z.[Zhi-Zhong],
Image compressive sensing via hybrid regularization combining
centralized group sparse representation and deep denoiser prior,
JVCIR(90), 2023, pp. 103723.
Elsevier DOI
2301
Image compressive sensing, Hybrid regularization,
Centralized group sparse representation, Deep denoiser prior
BibRef
Gan, H.P.[Hong-Ping],
Gao, Y.[Yang],
Liu, C.[Chunyi],
Chen, H.W.[Hai-Wei],
Zhang, T.[Tao],
Liu, F.[Feng],
AutoBCS: Block-Based Image Compressive Sensing With Data-Driven
Acquisition and Noniterative Reconstruction,
Cyber(53), No. 4, April 2023, pp. 2558-2571.
IEEE DOI
2303
Image reconstruction, Sensors, Transforms, Iterative algorithms,
Discrete wavelet transforms, Reconstruction algorithms,
image compressive sensing (CS)
BibRef
Yong, J.W.[Jia-Wei],
Li, K.[Kexin],
Feng, Z.J.[Zhe-Jun],
Wu, Z.Y.[Zeng-Yan],
Ye, S.B.[Shu-Bing],
Song, B.M.[Bao-Ming],
Wei, R.X.[Run-Xi],
Cao, C.Q.[Chang-Qing],
Research on Photon-Integrated Interferometric Remote Sensing Image
Reconstruction Based on Compressed Sensing,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link
2305
BibRef
Wang, Y.H.[Ying-Hua],
He, Z.[Zihao],
Zhang, G.M.[Guo-Ming],
Wen, J.M.[Jin-Ming],
Improved Sufficient Conditions Based on RIC of Order 2s for IHT and
HTP Algorithms,
SPLetters(30), 2023, pp. 668-672.
IEEE DOI
2307
iterative hard thresholding (IHT) and hard thresholding pursuit (HTP).
Signal processing algorithms, Indexes, Thresholding (Imaging),
Sparse matrices, Matching pursuit algorithms,
restricted isometry property
BibRef
Patel, S.[Saumya],
Vaish, A.[Ankita],
An efficient optimization of measurement matrix for compressive
sensing,
JVCIR(95), 2023, pp. 103904.
Elsevier DOI
2309
Measurement matrix, Optimization, Compressive sensing, Sparsity
BibRef
Meng, Z.Y.[Zi-Yi],
Yuan, X.[Xin],
Jalali, S.[Shirin],
Deep Unfolding for Snapshot Compressive Imaging,
IJCV(131), No. 1, January 2023, pp. 2933-2958.
Springer DOI
2310
BibRef
Qiu, W.W.[Wei-Wei],
Xue, L.L.[Lin-Lin],
Wang, Z.[Zhongpeng],
Recovery performance improvement of image compressive sensing using
complex-valued Vandermonde matrix,
IET-IPR(17), No. 13, 2023, pp. 3856-3868.
DOI Link
2311
compressed sensing, image reconstruction, Vandermonde matrix
BibRef
Chen, Y.R.[Yu-Rong],
Wang, Y.N.[Yao-Nan],
Zhang, H.[Hui],
Prior Image Guided Snapshot Compressive Spectral Imaging,
PAMI(45), No. 9, September 2023, pp. 11096-11107.
IEEE DOI
2309
BibRef
Zhao, Y.P.[Yin-Ping],
Zhang, J.C.[Jian-Cheng],
Chen, Y.Y.[Yong-Yong],
Wang, Z.[Zhen],
Li, X.L.[Xue-Long],
RCUMP: Residual Completion Unrolling With Mixed Priors for Snapshot
Compressive Imaging,
IP(33), 2024, pp. 2347-2360.
IEEE DOI
2404
Imaging, Image coding, Iterative methods, Optimization, Image reconstruction,
Hyperspectral imaging, deep unrolling-based methods
BibRef
Qiu, C.X.[Chen-Xi],
Hu, X.M.[Xue-Mei],
AdaCS: Adaptive Compressive Sensing With Restricted Isometry
Property-Based Error-Clamping,
PAMI(46), No. 7, July 2024, pp. 4702-4719.
IEEE DOI
2406
Image reconstruction, Imaging, Measurement uncertainty,
Magnetic resonance imaging, Adaptive systems, Loss measurement,
restricted isometry property
BibRef
Zhao, H.H.[Hui-Huang],
Zhang, L.[Lin],
Zhang, Y.D.[Yu-Dong],
Wang, Y.[Yaonan],
Imagery Overlap Block Compressive Sensing With Convex Optimization,
ITS(25), No. 7, July 2024, pp. 8076-8092.
IEEE DOI
2407
Image reconstruction, Compressed sensing, Image coding,
Matching pursuit algorithms, TV, Reconstruction algorithms, Poisson function
BibRef
Cao, J.H.[Jia-Hui],
Yang, Z.B.[Zhi-Bo],
Chen, X.F.[Xue-Feng],
Compressed Line Spectral Estimation Using Covariance:
A Sparse Reconstruction Perspective,
SPLetters(31), 2024, pp. 2540-2544.
IEEE DOI
2410
Covariance matrices, Sensors, Estimation, Coherence, Vectors,
Sparse matrices, Matrices, Compressed sensing,
periodic non-uniform sampling
BibRef
Chen, Y.R.[Yu-Rong],
Wang, Y.[Yaonan],
Zhang, H.[Hui],
Prior Images Guided Generative Autoencoder Model for Dual-Camera
Compressive Spectral Imaging,
CirSysVideo(34), No. 9, September 2024, pp. 8629-8643.
IEEE DOI
2410
Image reconstruction, Imaging, Hyperspectral imaging, Lenses,
Apertures, Relays, Optimization, Compressive spectral imaging,
compressive sensing
BibRef
Qu, G.[Gang],
Wang, P.[Ping],
Yuan, X.[Xin],
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging,
CVPR24(25327-25337)
IEEE DOI Code:
WWW Link.
2410
reconstruction from a few measurements captured by a single-pixel detector.
Tensors, Computational modeling, Source coding, Noise reduction,
Transformers, Cameras, Time measurement
BibRef
Zhang, J.C.[Jian-Cheng],
Zeng, H.[Haijin],
Cao, J.[Jiezhang],
Chen, Y.Y.[Yong-Yong],
Yu, D.X.[Deng-Xiu],
Zhao, Y.P.[Yin-Ping],
Dual Prior Unfolding for Snapshot Compressive Imaging,
CVPR24(25742-25752)
IEEE DOI Code:
WWW Link.
2410
Degradation, Image coding, Costs, Noise reduction, Imaging,
Iterative methods, image restoration
BibRef
Qiu, C.X.[Chen-Xi],
Yue, T.[Tao],
Hu, X.M.[Xue-Mei],
Reconstruction-free Cascaded Adaptive Compressive Sensing,
CVPR24(2620-2630)
IEEE DOI
2410
Training, Adaptive systems, Imaging,
Resource management, Image reconstruction
BibRef
Wang, X.Y.[Xiao-Yang],
Gan, H.P.[Hong-Ping],
UFC-Net: Unrolling Fixed-point Continuous Network for Deep
Compressive Sensing,
CVPR24(25149-25159)
IEEE DOI
2410
Correlation, Convolution, Imaging, Feature extraction, Resonance,
Iterative methods, Compressive Sensing, Deep Learning
BibRef
Pan, Z.H.[Zheng-Hao],
Zeng, H.[Haijin],
Cao, J.[Jiezhang],
Zhang, K.[Kai],
Chen, Y.Y.[Yong-Yong],
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative
Spectral Diffusion Model,
CVPR24(25297-25306)
IEEE DOI Code:
WWW Link.
2410
Image coding, Correlation, Noise reduction, Imaging,
Diffusion models, Transformers, Iterative algorithms
BibRef
Guo, Z.[Zhen],
Gan, H.P.[Hong-Ping],
CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding
CP-PPA Network for Compressive Sensing,
CVPR24(25086-25095)
IEEE DOI Code:
WWW Link.
2410
Deep learning, Fuses, Superresolution, Feature extraction,
Distortion, Iterative methods, Image reconstruction,
Deep Unfolding Networks
BibRef
Qin, X.R.[Xin-Ran],
Quan, Y.H.[Yu-Hui],
Pang, T.Y.[Tong-Yao],
Ji, H.[Hui],
Ground-Truth Free Meta-Learning for Deep Compressive Sampling,
CVPR23(9947-9956)
IEEE DOI
2309
BibRef
Dong, Y.[Yubo],
Gao, D.[Dahua],
Qiu, T.[Tian],
Li, Y.Y.[Yu-Yan],
Yang, M.X.[Min-Xi],
Shi, G.M.[Guang-Ming],
Residual Degradation Learning Unfolding Framework with Mixing Priors
Across Spectral and Spatial for Compressive Spectral Imaging,
CVPR23(22262-22271)
IEEE DOI
2309
BibRef
Hu, X.W.[Xiao-Wan],
Cai, Y.H.[Yuan-Hao],
Lin, J.[Jing],
Wang, H.Q.[Hao-Qian],
Yuan, X.[Xin],
Zhang, Y.[Yulun],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
HDNet: High-resolution Dual-domain Learning for Spectral Compressive
Imaging,
CVPR22(17521-17530)
IEEE DOI
2210
Photography, Learning systems, Visualization, Computational photography,
Time-frequency analysis, Smoothing methods
BibRef
Fan, Z.E.[Zi-En],
Lian, F.[Feng],
Quan, J.N.[Jia-Ni],
Global Sensing and Measurements Reuse for Image Compressed Sensing,
CVPR22(8944-8953)
IEEE DOI
2210
GSM, Convolutional codes, Computational modeling,
Benchmark testing, Feature extraction, Sensors,
Efficient learning and inferences
BibRef
Liu, J.L.[Jiu-Long],
Liu, Z.Q.[Zhao-Qiang],
Non-Iterative Recovery from Nonlinear Observations using Generative
Models,
CVPR22(233-243)
IEEE DOI
2210
Atmospheric measurements, Computational modeling,
Reconstruction algorithms, Particle measurements, Statistical methods
BibRef
Saideni, W.[Wael],
Courreges, F.[Fabien],
Helbert, D.[David],
Cances, J.P.[Jean Pierre],
End-to-End Video Snapshot Compressive Imaging using Video
Transformers,
IPTA22(1-6)
IEEE DOI
2206
Deep learning, Image coding, Memory management, Imaging,
Streaming media, Reconstruction algorithms, Transformers,
video compressive sensing
BibRef
Anirudh, R.[Rushil],
Lohit, S.[Suhas],
Turaga, P.[Pavan],
Generative Patch Priors for Practical Compressive Image Recovery,
WACV21(2534-2544)
IEEE DOI
2106
Image coding, Computational modeling, Generators, Sensors, Compressed sensing
BibRef
Yu, H.Y.[Hui-Yuan],
Cheng, M.[Maggie],
Lu, Y.D.[Ying-Dong],
A Randomized Algorithm for Sparse Recovery,
ICPR21(8312-8319)
IEEE DOI
2105
Approximation algorithms, Sparse matrices,
Optimization, Compressed sensing, Convergence
BibRef
Belyaev, E.,
Compressive Sensed Video Coding Having JPEG Compatibility,
ICIP20(1128-1132)
IEEE DOI
2011
Encoding, Streaming media, Codecs, Video coding, Transform coding,
Sensors, Loss measurement, video coding, compressive sensing
BibRef
Meng, Z.Y.[Zi-Yi],
Ma, J.W.[Jia-Wei],
Yuan, X.[Xin],
End-to-end Low Cost Compressive Spectral Imaging with Spatial-spectral
Self-attention,
ECCV20(XXIII:187-204).
Springer DOI
2011
BibRef
Bobin, J.,
Candes, E.J.,
A fast and accurate first-order algorithm for compressed sensing,
ICIP09(1457-1460).
IEEE DOI
0911
BibRef
Das, R.,
Rajwade, A.,
Nonlinear Blind Compressed Sensing Under Signal-Dependent Noise,
ICIP19(2030-2034)
IEEE DOI
1910
Blind Compressed Sensing, Anscombe Transform,
Multiplicative Update, Performance Bounds.
BibRef
Gao, Z.,
Ding, L.,
Xiong, C.,
Gong, Z.,
Xiong, Q.,
Compressive Sensing Reconstruction Based on Standardized Group Sparse
Representation,
ICIP19(2095-2099)
IEEE DOI
1910
Compressive sensing reconstruction, non local sparsity,
group sparse representation, z-score standardization
BibRef
Asif, M.S.,
Prater-Bennette, A.,
Multilinear Compressive Sensing With Tensor Ring Factorization,
ICIP19(2100-2104)
IEEE DOI
1910
BibRef
Hubbard-Featherstone, C.J.,
Garcia, M.A.,
Lee, W.Y.L.,
Adaptive block compressive sensing for image compression,
IVCNZ17(1-6)
IEEE DOI
1902
compressed sensing, image coding, image reconstruction,
image sampling, minimisation, transform coding, wavelet transforms,
discrete wavelet transform
BibRef
Yoshida, M.[Michitaka],
Torii, A.[Akihiko],
Okutomi, M.[Masatoshi],
Endo, K.[Kenta],
Sugiyama, Y.[Yukinobu],
Taniguchi, R.I.[Rin-Ichiro],
Nagahara, H.[Hajime],
Joint Optimization for Compressive Video Sensing and Reconstruction
Under Hardware Constraints,
ECCV18(X: 649-663).
Springer DOI
1810
BibRef
Güngör, A.,
Kar, O.F.,
Güven, H.E.,
A Matrix-Free Reconstruction Method for Compressive Focal Plane Array
Imaging,
ICIP18(1827-1831)
IEEE DOI
1809
Image reconstruction, Convergence, Image resolution, Cameras,
Sensor arrays, Complexity theory, Compressed Sensing,
Single Pixel Camera
BibRef
Akbari, A.,
Trocan, M.,
Robust Image Reconstruction for Block-Based Compressed Sensing Using
a Binary Measurement Matrix,
ICIP18(1832-1836)
IEEE DOI
1809
Image reconstruction, Matrix decomposition, Transforms,
Reconstruction algorithms, Sensors, Sparse matrices, Robustness,
image CS reconstruction
BibRef
Guimarães, J.P.F.[João P. F.],
Fontes, A.I.R.[Aluisio I. R.],
da Silva, F.B.[Felipe B.],
de M. Martins, A.[Allan],
von Borries, R.[Ricardo],
Complex Correntropy Induced Metric Applied to Compressive Sensing
with Complex-Valued Data,
Southwest18(21-24)
IEEE DOI
1809
Measurement, Kernel, Compressed sensing, Image reconstruction,
Robustness, Minimization, Approximation to l0,
compressive sensing
BibRef
Bernal, E.A.,
Li, Q.,
Tensorial compressive sensing of jointly sparse matrices with
applications to color imaging,
ICIP17(2781-2785)
IEEE DOI
1803
Color, Compressed sensing, Image color analysis,
Image reconstruction, Sparse matrices, Task analysis,
tensorial compressive sensing
BibRef
Li, Q.[Qun],
Bernal, E.A.[Edgar A.],
An Algorithm for Parallel Reconstruction of Jointly Sparse Tensors
with Applications to Hyperspectral Imaging,
PBVS17(218-225)
IEEE DOI
1709
Complexity theory, Hyperspectral imaging, Image reconstruction,
Matrix decomposition, Niobium, Tensile, stress
BibRef
Ren, M.J.[Meng-Jie],
Chen, S.[Shenpei],
Chen, D.[Dong],
Research on missile-borne image transmission technology based on
compressive sensing,
ICIVC17(773-777)
IEEE DOI
1708
Bandwidth, Encoding, Image coding, Image reconstruction, Sensors,
compressive sensing, missile-borne image, transmission, technology
BibRef
Sato, S.,
Wakai, N.,
Nobori, K.,
Azuma, T.,
Miyata, T.,
Nakashizuka, M.,
Compressive color sensing using random complementary color filter
array,
MVA17(43-46)
DOI Link
1708
Color, Compressed sensing, Image color analysis,
Image edge detection, Image reconstruction, Imaging, Optical, filters
BibRef
Wang, Z.E.[Zhong-Eng],
Chen, S.F.[Shou-Fa],
Performance comparison of image block compressive sensing based on
chaotic sensing matrix using different basis matrices,
ICIVC17(620-623)
IEEE DOI
1708
Compressed sensing, Discrete Fourier transforms,
Discrete cosine transforms, Discrete wavelet transforms,
Image reconstruction, Sparse matrices, compressive sensing,
image processing, peak signal-to-noise ratio, sparse, basis, matrix
BibRef
Jellali, Z.[Zakia],
Atallah, L.N.[Leïla Najjar],
Cherif, S.[Sofiane],
Data acquisition by 2D compression and ID reconstruction techniques
for WSN spatially correlated data,
ISIVC16(224-229)
IEEE DOI
1704
Correlation
BibRef
Kulkarni, K.[Kuldeep],
Lohit, S.[Suhas],
Turaga, P.K.[Pavan K.],
Kerviche, R.[Ronan],
Ashok, A.[Amit],
ReconNet: Non-Iterative Reconstruction of Images from Compressively
Sensed Measurements,
CVPR16(449-458)
IEEE DOI
1612
BibRef
Li, W.H.,
Yang, C.L.,
Ma, L.H.,
A multihypothesis-based residual reconstruction scheme in compressed
video sensing,
ICIP17(2766-2770)
IEEE DOI
1803
Indexes, SPL algorithm, compressed video sensing, multihypothesis, residual reconstruction
BibRef
Ou, W.F.,
Yang, C.L.,
Li, W.H.,
Ma, L.H.,
A two-stage multi-hypothesis reconstruction scheme in compressed
video sensing,
ICIP16(2494-2498)
IEEE DOI
1610
Adaptation models
BibRef
Zhao, C.,
Zhang, J.,
Ma, S.,
Xiong, R.,
Gao, W.,
A dual structured-sparsity model for compressive-sensed video
reconstruction,
VCIP15(1-4)
IEEE DOI
1605
Compressed sensing
BibRef
Ebrahim, M.,
Chai, W.C.,
Multi-phase joint reconstruction framework for multi-view video
compression using block-based compressive sensing,
VCIP15(1-4)
IEEE DOI
1605
Compressed sensing
BibRef
Xu, J.,
Djahel, S.,
Qiao, Y.,
Fu, Z.,
Perceptually-aware distributed compressive video sensing,
VCIP15(1-4)
IEEE DOI
1605
Codecs
BibRef
Zhang, Y.,
Comerford, L.,
Beer, M.,
Kougioumtzoglou, I.,
Compressive sensing for power spectrum estimation of
multi-dimensional processes under missing data,
WSSIP15(162-165)
IEEE DOI
1603
compressed sensing
BibRef
Ebadi, S.E.,
Izquierdo, E.,
Approximated RPCA for fast and efficient recovery of corrupted and
linearly correlated images and video frames,
WSSIP15(49-52)
IEEE DOI
1603
compressed sensing
BibRef
Chen, B.,
Perona, P.,
Seeing into Darkness: Scotopic Visual Recognition,
CVPR17(7292-7301)
IEEE DOI
1711
BibRef
Earlier:
Scotopic Visual Recognition,
Extreme15(659-662)
IEEE DOI
1602
Cameras, Photonics, Robustness, Sensors, Visualization.
Computational modeling. From a small number of photons.
BibRef
Che, W.B.[Wen-Bin],
Gao, X.W.[Xin-Wei],
Fan, X.P.[Xiao-Peng],
Jiang, F.[Feng],
Zhao, D.B.[De-Bin],
Spatial-temporal recovery for hierarchical frame based video
compressed sensing,
ICIP15(1110-1114)
IEEE DOI
1512
Video compressed sensing
BibRef
Francis, K.J.,
Rajalakshmi, P.,
Channappayya, S.S.[Sumohana S.],
Distributed compressed sensing for photo-acoustic imaging,
ICIP15(1513-1517)
IEEE DOI
1512
Distributed Compressive Sensing
BibRef
Tran, D.N.[Dung N.],
Tran, D.N.[Duyet N.],
Chin, S.P.[Sang Peter],
Tran, T.D.[Trac D.],
Local sensing with global recovery,
ICIP15(4313-4316)
IEEE DOI
1512
Compressed sensing; imaging; sparse representation
BibRef
Wang, J.[Jian],
Gupta, M.,
Sankaranarayanan, A.C.,
LiSens- A Scalable Architecture for Video Compressive Sensing,
ICCP15(1-9)
IEEE DOI
1511
data compression
BibRef
Spinoulas, L.[Leonidas],
Cossairt, O.[Oliver],
Katsaggelos, A.K.[Aggelos K.],
Sampling optimization for on-chip compressive video,
ICIP15(3329-3333)
IEEE DOI
1512
CMOS sensor; Sampling optimization; compressive sensing; high-speed video
BibRef
Spinoulas, L.[Leonidas],
He, K.[Kuan],
Cossairt, O.[Oliver],
Katsaggelos, A.K.[Aggelos K.],
Video compressive sensing with on-chip programmable subsampling,
CCD15(49-57)
IEEE DOI
1510
Cameras
BibRef
Chen, H.[Huaijin],
Asif, M.S.[M.Salman],
Sankaranarayanan, A.C.[Aswin C.],
Veeraraghavan, A.[Ashok],
FPA-CS: Focal plane array-based compressive imaging in short-wave
infrared,
CVPR15(2358-2366)
IEEE DOI
1510
BibRef
Sato, S.[Satoshi],
Ishii, M.[Motonori],
Kato, Y.[Yoshihisa],
Nobori, K.[Kunio],
Azuma, T.[Takeo],
Compressive sensing reconstruction using collaborative sparsity among
color channels,
MVA15(406-409)
IEEE DOI
1507
Cameras
BibRef
Mourchid, Y.,
El Hassouni, M.,
Comparative study between different bases of transformation for
compressive sensing of images,
ISCV15(1-7)
IEEE DOI
1506
compressed sensing
BibRef
Dong, H.F.[Hai-Feng],
Zhuang, B.[Bojin],
Su, F.[Fei],
Zhao, Z.C.[Zhi-Cheng],
A novel distributed compressive video sensing based on hybrid sparse
basis,
VCIP14(320-323)
IEEE DOI
1504
compressed sensing
BibRef
Zhao, C.[Chen],
Ma, S.W.[Si-Wei],
Gao, W.[Wen],
Video compressive sensing via structured Laplacian modelling,
VCIP14(402-405)
IEEE DOI
1504
Laplace equations
BibRef
Kerviche, R.[Ronan],
Zhu, N.[Nan],
Ashok, A.[Amit],
Information optimal scalable compressive imager demonstrator,
ICIP14(2177-2179)
IEEE DOI
1502
System delivers high-resolution images from low resolution sensor
with near real-time snapshots.
BibRef
Hou, Y.[Ying],
Zhang, Y.N.[Yan-Ning],
Effective Image Block Compressed Sensing,
ICPR14(1085-1090)
IEEE DOI
1412
Compressed sensing
BibRef
Li, Y.[Yong],
Xiong, H.K.[Hong-Kai],
Ye, X.W.[Xin-Wei],
Compressive video sampling from a union of data-driven subspaces,
VCIP13(1-6)
IEEE DOI
1402
compressed sensing
Sample and recover an unknown signal from a union
of data-driven subspaces.
BibRef
Li, X.W.[Xiang-Wei],
Lan, X.G.[Xu-Guang],
Yang, M.[Meng],
Xue, J.R.[Jian-Ru],
Zheng, N.N.[Nan-Ning],
Optimized truncation model for adaptive compressive sensing
acquisition of images,
VCIP15(1-4)
IEEE DOI
1605
BibRef
Earlier:
Universal and low-complexity quantizer design for compressive sensing
image coding,
VCIP13(1-5)
IEEE DOI
1402
Adaptation models.
codecs
BibRef
Dinh, K.Q.[Khanh Quoc],
Shim, H.J.[Hiuk Jae],
Jeon, B.W.[Byeung-Woo],
Measurement coding for compressive imaging using a structural
measuremnet matrix,
ICIP13(10-13)
IEEE DOI
1402
Compressed sensing
BibRef
Iliadis, M.[Michael],
Watt, J.[Jeremy],
Spinoulas, L.[Leonidas],
Katsaggelos, A.K.[Aggelos K.],
Video compressive sensing using multiple measurement vectors,
ICIP13(136-140)
IEEE DOI
1402
Compressed sensing
BibRef
Anaraki, F.P.[Farhad Pourkamali],
Hughes, S.M.[Shannon M.],
Kernel compressive sensing,
ICIP13(494-498)
IEEE DOI
1402
Compressed sensing
BibRef
Yang, F.[Fei],
Jiang, H.[Hong],
Shen, Z.[Zuowei],
Deng, W.[Wei],
Metaxas, D.N.[Dimitris N.],
Adaptive low rank and sparse decomposition of video using compressive
sensing,
ICIP13(1016-1020)
IEEE DOI
1402
Cameras
BibRef
Zhang, J.[Jian],
Zhao, D.B.[De-Bin],
Jiang, F.[Feng],
Spatially directional predictive coding for block-based compressive
sensing of natural images,
ICIP13(1021-1025)
IEEE DOI
1402
Bit rate
BibRef
Hua, B.S.[Binh-Son],
Sato, I.[Imari],
Low, K.L.[Kok-Lim],
Direct and progressive reconstruction of dual photography images,
ICIP13(3157-3161)
IEEE DOI
1402
compressive sensing;dual photography
BibRef
Chu, X.Y.[Xiao-Yu],
Stamm, M.C.[Matthew C.],
Liu, K.J.R.[K. J. Ray],
Forensic identification of compressively sensed signals,
ICIP12(257-260).
IEEE DOI
1302
BibRef
Zhang, X.Y.[Xin-Yu],
Wen, J.T.[Jiang-Tao],
Compressive video sensing using non-linear mapping,
ICIP12(885-888).
IEEE DOI
1302
BibRef
Rao, N.S.[Nikhil S.],
Nowak, R.D.[Robert D.],
Correlated gaussian designs for compressive imaging,
ICIP12(921-924).
IEEE DOI
1302
BibRef
Golbabaee, M.[Mohammad],
Vandergheynst, P.[Pierre],
Joint trace/TV norm minimization:
A new efficient approach for spectral compressive imaging,
ICIP12(933-936).
IEEE DOI
1302
BibRef
Kumar, N.R.,
Xiang, W.[Wei],
Soar, J.,
A Novel Image Compressive Sensing Method Based on Complex Measurements,
DICTA11(175-179).
IEEE DOI
1205
BibRef
Papandreou, G.[George],
Yuille, A.L.[Alan L.],
Efficient variational inference in large-scale Bayesian compressed
sensing,
ITCVPR11(1332-1339).
IEEE DOI
1201
BibRef
Shu, X.B.[Xian-Biao],
Yang, J.C.[Jian-Chao],
Ahuja, N.,
Non-local compressive sampling recovery,
ICCP14(1-8)
IEEE DOI
1411
compressed sensing
BibRef
Shu, X.B.[Xian-Biao],
Ahuja, N.[Narendra],
Imaging via three-dimensional compressive sampling (3DCS),
ICCV11(439-446).
IEEE DOI
1201
I.e. sample below Nyquist rate. A lot of theory, but no camera yet.
BibRef
Le Montagner, Y.[Yoann],
Marim, M.M.[Marcio M.],
Angelini, E.D.[Elsa D.],
Olivo-Marin, J.C.[Jean-Christophe],
Numerical evaluation of sampling bounds for near-optimal reconstruction
in compressed sensing,
ICIP11(3073-3076).
IEEE DOI
1201
BibRef
Li, B.[Bin],
Zhu, X.[Xuqi],
Liu, Y.[Yu],
Zhang, L.[Lin],
An unequally protected Distributed Compressed Video Sensing algorithm,
VCIP11(1-4).
IEEE DOI
1201
BibRef
Huang, H.L.[Hong-Lin],
Makur, A.,
Venkatraman, D.,
Video object error coding method based on compressive sensing,
ICARCV08(1287-1291).
IEEE DOI
1109
BibRef
Xiao, L.[Liang],
Shao, J.[Jun],
Huang, L.[Lili],
Wei, Z.H.[Zhi-Hui],
Compounded Regularization and Fast Algorithm for Compressive Sensing
Deconvolution,
ICIG11(616-621).
IEEE DOI
1109
BibRef
Lin, X.F.[Xiao-Fen],
Lu, G.[Gang],
Yan, J.W.[Jing-Wen],
Lin, W.[Wei],
Measurement Matrix of Compressive Sensing Based on Gram-Schmidt
Orthogonalization,
ICIG11(205-210).
IEEE DOI
1109
BibRef
Ren, Y.M.[Yue-Mei],
Zhang, Y.N.[Yang-Ning],
Li, Y.[Ying],
Huang, J.Y.[Jian-Yu],
Hui, J.J.[Jian-Jiang],
A Space Target Recognition Method Based on Compressive Sensing,
ICIG11(582-586).
IEEE DOI
1109
BibRef
Borghi, A.,
Darbon, J.,
Peyronnet, S.,
Chan, T.F.[Tony F.],
Osher, S.J.[Stanley J.],
A Compressive Sensing Algorithm for Many-Core Architectures,
ISVC10(II: 678-686).
Springer DOI
1011
BibRef
Trocan, M.[Maria],
Maugey, T.[Thomas],
Tramel, E.W.[Eric W.],
Fowler, J.E.[James E.],
Pesquet-Popescu, B.[Beatrice],
Compressed sensing of multiview images using disparity compensation,
ICIP10(3345-3348).
IEEE DOI
1009
BibRef
Zhang, X.[Xin],
Lam, E.Y.[Edmund Y.],
Sectional image reconstruction in optical scanning holography using
compressed sensing,
ICIP10(3349-3352).
IEEE DOI
1009
BibRef
He, Z.X.[Zai-Xing],
Ogawa, T.[Takahiro],
Haseyama, M.[Miki],
The simplest measurement matrix for compressed sensing of natural
images,
ICIP10(4301-4304).
IEEE DOI
1009
BibRef
Majumdar, A.[Angshul],
Ward, R.K.[Rabab K.],
Compressive color imaging with group-sparsity on analysis prior,
ICIP10(1337-1340).
IEEE DOI
1009
BibRef
Shang, F.[Fei],
Du, H.Q.[Hui-Qian],
Jia, Y.D.[Yun-De],
Compressive Sampling Recovery for Natural Images,
ICPR10(2206-2209).
IEEE DOI
1008
BibRef
Yamamoto, S.[Satoshi],
Itakura, Y.[Yasumasa],
Sawabe, M.[Masashi],
Precomputed ROMP for light transport acquisition,
PROCAMS10(49-56).
IEEE DOI
1006
Compressive sensing.
BibRef
Chang, H.S.[Hyun Sung],
Weiss, Y.[Yair],
Freeman, W.T.[William T.],
Informative sensing of natural images,
ICIP09(3025-3028).
IEEE DOI
0911
different random sampling.
BibRef
Mun, S.K.[Sung-Kwang],
Fowler, J.E.[James E.],
Block compressed sensing of images using directional transforms,
ICIP09(3021-3024).
IEEE DOI
0911
BibRef
Lu, W.[Wei],
Vaswani, N.[Namrata],
Modified compressive sensing for real-time dynamic MR imaging,
ICIP09(3045-3048).
IEEE DOI
0911
BibRef
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Attention-guided Image Compression by Deep Reconstruction of
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CVPR21(13349-13359)
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2111
Deep learning, Image coding, Codes, Skeleton,
Image reconstruction
BibRef
Wu, X.L.[Xiao-Lin],
Zhang, X.J.[Xiang-Jun],
Compressive-uniform hybrid sensing for image acquisition and
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ICIP09(3041-3044).
IEEE DOI
0911
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Wang, T.[Tao],
Zhu, Z.G.[Zhi-Gang],
Rhody, H.[Harvey],
A smart sensor with hyperspectral/range fovea and panoramic peripheral
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OTCBVS09(98-105).
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0906
BibRef
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Canagarajah, N.[Nishan],
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Compressive image fusion,
ICIP08(1308-1311).
IEEE DOI
0810
Sampling pattern.
BibRef
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Easley, G.R.[Glenn R.],
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ICIP09(3033-3036).
IEEE DOI
0911
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A fast decoder for Compressed Sensing based multiple description image
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ICIP09(2125-2128).
IEEE DOI
0911
BibRef
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da Silva, E.A.B.[Eduardo A.B.],
On the empirical rate-distortion performance of Compressive Sensing,
ICIP09(3049-3052).
IEEE DOI
0911
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Coded Aperture Compressive Sensing .