Yu, N.Y.[Nam Yul],
Gan, L.[Lu],
Convolutional Compressed Sensing Using Decimated Sidelnikov Sequences,
SPLetters(21), No. 5, May 2014, pp. 591-594.
IEEE DOI
1404
Gaussian processes
BibRef
Fang, J.,
Li, J.,
Shen, Y.,
Li, H.,
Li, S.,
Super-Resolution Compressed Sensing: An Iterative Reweighted
Algorithm for Joint Parameter Learning and Sparse Signal Recovery,
SPLetters(21), No. 6, June 2014, pp. 761-765.
IEEE DOI
1404
Compressed sensing
BibRef
Yang, J.B.[Jian-Bo],
Liao, X.J.[Xue-Jun],
Yuan, X.[Xin],
Llull, P.[Patrick],
Brady, D.J.[David J.],
Sapiro, G.[Guillermo],
Carin, L.[Lawrence],
Compressive Sensing by Learning a Gaussian Mixture Model From
Measurements,
IP(24), No. 1, January 2015, pp. 106-119.
IEEE DOI
1502
Gaussian processes
BibRef
Earlier: A1, A3, A2, A4, A6, A5, A7:
Gaussian mixture model for video compressive sensing,
ICIP13(19-23)
IEEE DOI
1402
BibRef
Yang, J.B.[Jian-Bo],
Yuan, X.[Xin],
Liao, X.J.[Xue-Jun],
Llull, P.[Patrick],
Brady, D.J.[David J.],
Sapiro, G.[Guillermo],
Carin, L.[Lawrence],
Video Compressive Sensing Using Gaussian Mixture Models,
IP(23), No. 11, November 2014, pp. 4863-4878.
IEEE DOI
1410
BibRef
Earlier: A2, A1, A4, A3, A6, A5, A7:
Adaptive temporal compressive sensing for video,
ICIP13(14-18)
IEEE DOI
1402
Cameras
BibRef
Schwartz, S.[Shimon],
Wong, A.[Alexander],
Clausi, D.A.[David A.],
Optimized sampling distribution based on nonparametric learning for
improved compressive sensing performance,
JVCIR(31), No. 1, 2015, pp. 26-40.
Elsevier DOI
1508
Compressed sampling
BibRef
Zayyani, H.,
Korki, M.,
Marvasti, F.,
Dictionary Learning for Blind One Bit Compressed Sensing,
SPLetters(23), No. 2, February 2016, pp. 187-191.
IEEE DOI
1602
compressed sensing.
the original signal to be reconstructed from one bit linear random
measurements is sparse in an unknown domain.
BibRef
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Shi, Q.F.[Qin-Feng],
Dictionary Learning for Promoting Structured Sparsity in
Hyperspectral Compressive Sensing,
GeoRS(54), No. 12, December 2016, pp. 7223-7235.
IEEE DOI
1612
Bayes methods
BibRef
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Shi, Q.F.[Qin-Feng],
Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for
Reconstruction,
IJCV(126), No. 8, August 2018, pp. 797-821.
Springer DOI
1807
BibRef
And:
Cluster Sparsity Field for Hyperspectral Imagery Denoising,
ECCV16(V: 631-647).
Springer DOI
1611
BibRef
Zhang, L.[Lei],
Wei, W.[Wei],
Tian, C.N.[Chun-Na],
Li, F.[Fei],
Zhang, Y.N.[Yan-Ning],
Exploring Structured Sparsity by a Reweighted Laplace Prior for
Hyperspectral Compressive Sensing,
IP(25), No. 10, October 2016, pp. 4974-4988.
IEEE DOI
1610
BibRef
Earlier: A1, A2, A5, A3, A4:
Reweighted laplace prior based hyperspectral compressive sensing for
unknown sparsity,
CVPR15(2274-2281)
IEEE DOI
1510
Bayes methods
BibRef
Wei, W.[Wei],
Zhang, L.[Lei],
Tian, C.N.[Chun-Na],
Plaza, A.[Antonio],
Zhang, Y.N.[Yan-Ning],
Structured Sparse Coding-Based Hyperspectral Imagery Denoising With
Intracluster Filtering,
GeoRS(55), No. 12, December 2017, pp. 6860-6876.
IEEE DOI
1712
Covariance matrices, Dictionaries, Encoding, Hyperspectral imaging,
Noise measurement, Noise reduction, Tensile stress,
structured sparse coding
BibRef
Wang, C.[Cong],
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.[Yanning],
When Low Rank Representation Based Hyperspectral Imagery
Classification Meets Segmented Stacked Denoising Auto-Encoder Based
Spatial-Spectral Feature,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Zhang, L.[Lei],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Li, F.[Fei],
Shen, C.H.[Chun-Hua],
Shi, Q.F.[Qin-Feng],
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity
Prior,
ICCV15(3550-3558)
IEEE DOI
1602
Correlation
BibRef
Zhao, Z.F.[Zhi-Fu],
Xie, X.M.[Xue-Mei],
Wang, C.Y.[Chen-Ye],
Mao, S.Y.[Si-Ying],
Liu, W.[Wan],
Shi, G.M.[Guang-Ming],
ROI-CSNet: Compressive sensing network for ROI-aware image recovery,
SP:IC(78), 2019, pp. 113-124.
Elsevier DOI
1909
Compressive sensing (CS), Region of interest (ROI),
Convolutional neural network (CNN)
BibRef
Kaur, A.,
Mishra, D.,
Amogh, K.M.,
Sarkar, M.,
On-Array Compressive Acquisition in CMOS Image Sensors Using
Accumulated Spatial Gradients,
CirSysVideo(31), No. 2, February 2021, pp. 523-532.
IEEE DOI
2102
Image coding, Image sensors, Silicon, Image reconstruction,
Interpolation, Discrete cosine transforms, Hardware,
deep convolution neural network
BibRef
Zhou, S.W.[Si-Wang],
He, Y.[Yan],
Liu, Y.H.[Yong-He],
Li, C.Q.[Cheng-Qing],
Zhang, J.M.[Jian-Ming],
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing,
MultMed(23), 2021, pp. 2627-2640.
IEEE DOI
2109
Image reconstruction, Sensors, Correlation,
Approximation algorithms, Smoothing methods, Neural networks, image recovery
BibRef
Guo, Y.[Yuan],
Jiang, J.L.[Jin-Lin],
Chen, W.[Wei],
Fast bilateral complementary network for deep learning compressed
sensing image reconstruction,
IET-IPR(16), No. 13, 2022, pp. 3485-3498.
DOI Link
2210
BibRef
Huang, T.[Tao],
Yuan, X.[Xin],
Dong, W.S.[Wei-Sheng],
Wu, J.J.[Jin-Jian],
Shi, G.M.[Guang-Ming],
Deep Gaussian Scale Mixture Prior for Image Reconstruction,
PAMI(45), No. 9, September 2023, pp. 10778-10794.
IEEE DOI
2309
BibRef
Earlier: A1, A3, A2, A4, A5:
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,
CVPR21(16211-16220)
IEEE DOI
2111
GSM, Training, Image coding, Imaging, Estimation,
Reconstruction algorithms, Apertures
BibRef
Yang, X.[Xin],
Yang, C.L.[Chun-Ling],
MAP-Inspired Deep Unfolding Network for Distributed Compressive Video
Sensing,
SPLetters(30), 2023, pp. 309-313.
IEEE DOI
2304
Image reconstruction, Correlation, Optical imaging, Adaptive optics,
Optical signal processing, Iterative methods, multi-hypothesis
BibRef
Song, J.C.[Jie-Chong],
Chen, B.[Bin],
Zhang, J.[Jian],
Dynamic Path-Controllable Deep Unfolding Network for Compressive
Sensing,
IP(32), 2023, pp. 2202-2214.
IEEE DOI
2305
Heuristic algorithms, Optimization, Image reconstruction,
Image coding, Compressed sensing, Image restoration, dynamic modulation
BibRef
Song, J.C.[Jie-Chong],
Chen, B.[Bin],
Zhang, J.[Jian],
Deep Memory-Augmented Proximal Unrolling Network for Compressive
Sensing,
IJCV(131), No. 6, June 2023, pp. 1477-1496.
Springer DOI
2305
BibRef
Ye, D.J.[Dong-Jie],
Ni, Z.K.[Zhang-Kai],
Wang, H.[Hanli],
Zhang, J.[Jian],
Wang, S.Q.[Shi-Qi],
Kwong, S.[Sam],
CSformer: Bridging Convolution and Transformer for Compressive Sensing,
IP(32), 2023, pp. 2827-2842.
IEEE DOI
2306
Transformers, Image reconstruction, Convolutional neural networks,
Convolution, Deep learning, image reconstruction
BibRef
Yamaç, M.[Mehmet],
Akpinar, U.[Ugur],
Sahin, E.[Erdem],
Kiranyaz, S.[Serkan],
Gabbouj, M.[Moncef],
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An
Easy to Learn Compressive Sensing Operation,
IP(32), 2023, pp. 5637-5651.
IEEE DOI Code:
WWW Link.
2310
BibRef
Zhang, K.Y.[Kui-Yuan],
Hua, Z.Y.[Zhong-Yun],
Li, Y.M.[Yuan-Man],
Chen, Y.Y.[Yong-Yong],
Zhou, Y.C.[Yi-Cong],
AMS-Net: Adaptive Multi-Scale Network for Image Compressive Sensing,
MultMed(25), 2023, pp. 5676-5689.
IEEE DOI
2311
BibRef
Xu, H.,
Zhang, C.,
Kim, I.,
Coupled Online Robust Learning of Observation and Dictionary for
Adaptive Analog-to-Information Conversion,
SPLetters(26), No. 1, January 2019, pp. 139-143.
IEEE DOI
1901
compressed sensing, learning (artificial intelligence),
optimisation, signal denoising, signal reconstruction,
robust dictionary learning
BibRef
Fu, W.,
Lu, T.,
Li, S.,
Context-Aware Compressed Sensing of Hyperspectral Image,
GeoRS(58), No. 1, January 2020, pp. 268-280.
IEEE DOI
2001
Image reconstruction, Dictionaries, Hyperspectral imaging, Imaging,
Machine learning, Sparse matrices, Compressed sensing (CS),
sparse reconstruction
BibRef
Sun, Y.B.[Yu-Bao],
Chen, J.W.[Ji-Wei],
Liu, Q.S.[Qing-Shan],
Liu, B.[Bo],
Guo, G.D.[Guo-Dong],
Dual-Path Attention Network for Compressed Sensing Image
Reconstruction,
IP(29), 2020, pp. 9482-9495.
IEEE DOI
1806
Image reconstruction, Optimization, Compressed sensing,
Machine learning, Iterative methods, Periodic structures,
texture attention
BibRef
Sun, Y.B.[Yu-Bao],
Yang, Y.,
Liu, Q.S.[Qing-Shan],
Chen, J.W.[Ji-Wei],
Yuan, X.T.,
Guo, G.D.[Guo-Dong],
Learning Non-Locally Regularized Compressed Sensing Network With
Half-Quadratic Splitting,
MultMed(22), No. 12, December 2020, pp. 3236-3248.
IEEE DOI
2011
Image reconstruction, Image sequences, Compressed sensing,
Training, Machine learning, Electronics packaging,
half-quadratic splitting
BibRef
Shi, W.Z.[Wu-Zhen],
Jiang, F.[Feng],
Liu, S.H.[Shao-Hui],
Zhao, D.B.[De-Bin],
Image Compressed Sensing Using Convolutional Neural Network,
IP(29), No. 1, 2020, pp. 375-388.
IEEE DOI
1910
BibRef
Earlier:
Multi-Scale Deep Networks for Image Compressed Sensing,
ICIP18(46-50)
IEEE DOI
1809
compressed sensing, convolutional neural nets,
image reconstruction, image sampling.
Convolution, Training,
Computational modeling, Image coding, Mean square error methods,
BibRef
Shi, W.Z.[Wu-Zhen],
Liu, S.H.[Shao-Hui],
Jiang, F.[Feng],
Zhao, D.B.[De-Bin],
Video Compressed Sensing Using a Convolutional Neural Network,
CirSysVideo(31), No. 2, February 2021, pp. 425-438.
IEEE DOI
2102
BibRef
Earlier: A1, A3, A2, A4:
Scalable Convolutional Neural Network for Image Compressed Sensing,
CVPR19(12282-12291).
IEEE DOI
2002
Image reconstruction, Correlation, Compressed sensing,
Convolutional neural networks,
convolutional neural network
BibRef
Cui, W.X.[Wen-Xue],
Liu, S.H.[Shao-Hui],
Jiang, F.[Feng],
Zhao, D.B.[De-Bin],
Image Compressed Sensing Using Non-Local Neural Network,
MultMed(25), 2023, pp. 816-830.
IEEE DOI
2303
Image reconstruction, Neural networks, Reconstruction algorithms,
Training, Noise reduction, Iterative algorithms,
non-local self-similarity prior
BibRef
Cui, W.X.[Wen-Xue],
Jiang, F.[Feng],
Gao, X.W.[Xin-Wei],
Tao, W.[Wen],
Zhao, D.B.[De-Bin],
Deep Neural Network Based Sparse Measurement Matrix for Image
Compressed Sensing,
ICIP18(3883-3887)
IEEE DOI
1809
Image reconstruction, Sparse matrices, Training, Neural networks,
Compressed sensing, Kernel, Memory management, Compressed sensing,
sparsity
BibRef
Yang, Y.[Yan],
Sun, J.[Jian],
Li, H.B.[Hui-Bin],
Xu, Z.B.[Zong-Ben],
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing,
PAMI(42), No. 3, March 2020, pp. 521-538.
IEEE DOI
2002
Image reconstruction, Transforms, Imaging, Task analysis,
Data models, Compressive sensing,
ADMM-CSNet
BibRef
Su, Y.M.[Yue-Ming],
Lian, Q.S.[Qiu-Sheng],
iPiano-Net: Nonconvex optimization inspired multi-scale
reconstruction network for compressed sensing,
SP:IC(89), 2020, pp. 115989.
Elsevier DOI
2010
iPiano algorithm, Compressed sensing,
Convolutional neural network, Deep learning
BibRef
Barajas-Solano, C.[Crisostomo],
Ramirez, J.M.[Juan-Marcos],
Arguello, H.[Henry],
Convolutional sparse coding framework for compressive spectral
imaging,
JVCIR(66), 2020, pp. 102690.
Elsevier DOI
2003
Compressive spectral imaging, Convolutional sparse coding,
Sparse representation, Spectral images
BibRef
Barajas-Solano, C.[Crisostomo],
Ramirez, J.M.[Juan-Marcos],
Torre, J.I.M.[José Ignacio Martínez],
Arguello, H.[Henry],
Compressive Spectral Video Sensing using the Convolutional Sparse
Coding framework CSC4D,
JVCIR(92), 2023, pp. 103782.
Elsevier DOI
2303
Compressive spectral video sensing,
Convolutional sparse coding, Sparse representation, Spectral videos
BibRef
Rueda, H.[Hoover],
Arguello, H.[Henry],
Arce, G.R.[Gonzalo R.],
Dual-ARM VIS/NIR compressive spectral imager,
ICIP15(2572-2576)
IEEE DOI
1512
Compressive sensing
BibRef
Xie, Y.H.[Ya-Hong],
Wang, H.L.[Hai-Lin],
Wang, J.J.[Jian-Jun],
CMCS-net: Image Compressed Sensing with Convolutional Measurement via
DCNN,
IET-IPR(14), No. 15, 15 December 2020, pp. 3839-3850.
DOI Link
2103
BibRef
Nguyen, v.[van_Thien],
Guicquero, W.[William],
Sicard, G.[Gilles],
A 1Mb Mixed-Precision Quantized Encoder for Image Classification and
Patch-Based Compression,
CirSysVideo(32), No. 8, August 2022, pp. 5581-5594.
IEEE DOI
2208
Quantization (signal), Image coding, Hardware, Task analysis,
Convolution, Neural networks, Training,
patch-based image compression
BibRef
Benjilali, W.,
Guicquero, W.[William],
Jacques, L.,
Sicard, G.[Gilles],
Hardware-Friendly Compressive Imaging Based on Random Modulations
Permutations for Image Acquisition and Classification,
ICIP19(2085-2089)
IEEE DOI
1910
Compressive sensing, random modulations, random permutations,
image sensor, machine learning
BibRef
Chen, B.[Bin],
Zhang, J.[Jian],
Content-Aware Scalable Deep Compressed Sensing,
IP(31), 2022, pp. 5412-5426.
IEEE DOI
2208
Resource management, Scalability, Training, Image restoration,
Image reconstruction, Task analysis, Detectors, Compressed sensing,
deep unfolding network
BibRef
Xu, S.P.[Shao-Ping],
Cheng, X.H.[Xiao-Hui],
Luo, J.[Jie],
Xiao, N.[Nan],
Xiong, M.H.[Ming-Hai],
Zhou, C.F.[Chang-Fei],
Dual-branch deep image prior for image denoising,
JVCIR(93), 2023, pp. 103821.
Elsevier DOI
2305
Image denoising, Boosting performance, Dual-branch architecture,
Two-stage denoising, Basic images, Unsupervised fusion
BibRef
Guo, H.F.[Hai-Feng],
Kwong, S.[Sam],
Jia, C.M.[Chuan-Min],
Wang, S.Q.[Shi-Qi],
Enhanced Motion Compensation for Deep Video Compression,
SPLetters(30), 2023, pp. 673-677.
IEEE DOI
2307
Motion compensation, Video compression, Bit rate,
Electromagnetic compatibility, Predictive models,
enhanced motion compensation
BibRef
Cui, W.X.[Wen-Xue],
Fan, X.P.[Xiao-Peng],
Zhang, J.[Jian],
Zhao, D.B.[De-Bin],
Deep Unfolding Network for Image Compressed Sensing by
Content-Adaptive Gradient Updating and Deformation-Invariant
Non-Local Modeling,
MultMed(26), 2024, pp. 4012-4027.
IEEE DOI
2402
Image reconstruction, Compressed sensing, Adaptation models, Deformable models,
Image coding, Adaptive systems, Limiting, proximal gradient descent (PGD)
BibRef
Gu, Z.F.[Zhen-Fei],
Zhou, C.[Chao],
Lin, G.F.[Guo-Feng],
A temporal shift reconstruction network for compressive video sensing,
IET-CV(18), No. 4, 2024, pp. 448-457.
DOI Link
2406
Compressive video sensing.
compressed sensing, image reconstruction, neural nets, video coding
BibRef
Su, Y.M.[Yue-Ming],
Lian, Q.S.[Qiu-Sheng],
Zhang, D.[Dan],
Shi, B.S.[Bao-Shun],
Transformer based Douglas-Rachford unrolling network for compressed
sensing,
SP:IC(127), 2024, pp. 117153.
Elsevier DOI Code:
WWW Link.
2408
Compressed sensing, Transformer, Binary sampling,
Douglas-Rachford algorithm, Deep learning
BibRef
Li, W.Q.[Wei-Qi],
Chen, B.[Bin],
Liu, S.[Shuai],
Zhao, S.J.[Shi-Jie],
Du, B.[Bowen],
Zhang, Y.B.[Yong-Bing],
Zhang, J.[Jian],
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for
Compressive Sensing,
CirSysVideo(34), No. 10, October 2024, pp. 9341-9355.
IEEE DOI Code:
WWW Link.
2411
Convolutional codes, Convolution, Optimization, Encoding,
Artificial neural networks, Image reconstruction, Image coding,
deep unfolding network
BibRef
Beye, F.[Florian],
Itsumi, H.[Hayato],
Vitthal, C.[Charvi],
Nihei, K.[Koichi],
Recognition-Aware Deep Video Compression for Remote Surveillance,
ICIP22(1986-1990)
IEEE DOI
2211
Image quality, Visualization, Image coding, Quantization (signal),
Wireless networks, Bit rate, Video sequences, Video Compression,
Object Detection
BibRef
Chen, J.W.[Ji-Wei],
Sun, Y.B.[Yu-Bao],
Liu, Q.S.[Qing-Shan],
Huang, R.[Rui],
Learning Memory Augmented Cascading Network for Compressed Sensing of
Images,
ECCV20(XXII:513-529).
Springer DOI
2011
BibRef
Gupta, P.S.,
Yuan, X.,
Choi, G.S.,
DRCAS: Deep Restoration Network for Hardware Based Compressive
Acquisition Scheme,
ICIP20(291-295)
IEEE DOI
2011
Image coding, Transform coding, Image resolution,
Image restoration, Image reconstruction, Image sensors, Hardware,
image restoration
BibRef
Pei, H.,
Yang, C.,
Cao, Y.,
Deep Smoothed Projected Landweber Network for Block-Based Image
Compressive Sensing,
ICIP20(2870-2874)
IEEE DOI
2011
Image reconstruction, Integrated circuits, Convolution, Transforms,
Compressed sensing, Training, Gray-scale,
image reconstruction
BibRef
Yamada, M.,
Adachi, H.,
Horisaki, R.,
Sato, I.,
A Comparison of Compressed Sensing and DNN Based Reconstruction For
Ghost Motion Imaging,
ICIP20(2910-2914)
IEEE DOI
2011
Image reconstruction, Optical imaging, Optical detectors,
Detectors, Throughput, Ghost Imaging, Ghost Motion Imaging,
Deep Learning
BibRef
Sogabe, Y.,
Sugimoto, S.,
Kurozumi, T.,
Kimata, H.,
ADMM-Inspired Reconstruction Network for Compressive Spectral Imaging,
ICIP20(2865-2869)
IEEE DOI
2011
Image reconstruction, Hyperspectral imaging, Convex functions,
Image coding, Imaging, Convergence, Iterative methods,
Compressed Sensing
BibRef
Li, W.,
Li, S.,
Liu, R.,
Channel Shuffle Reconstruction Network for Image Compressive Sensing,
ICIP20(2880-2884)
IEEE DOI
2011
Indexes, Economic indicators, Image compressive sensing,
Inverted residual, Channel shuffle, Multi-scale
BibRef
Yuan, X.[Xin],
Ren, L.L.[Liang-Liang],
Lu, J.W.[Ji-Wen],
Zhou, J.[Jie],
Enhanced Bayesian Compression via Deep Reinforcement Learning,
CVPR19(6939-6948).
IEEE DOI
2002
BibRef
Xie, Y.,
Wang, Z.,
Pei, W.,
Tang, G.,
Fast Approximation of Non-Negative Sparse Recovery via Deep Learning,
ICIP19(2921-2925)
IEEE DOI
1910
Deep learning, algorithm approximation,
non-negative sparse recovery, compressive sensing
BibRef
Reddy K, P.K.,
Chaudhury, K.N.,
Learning Iteration-Dependent Denoisers for Model-Consistent
Compressive Sensing,
ICIP19(2090-2094)
IEEE DOI
1910
compressive sensing, noise model, denoising, deep neural networks, consistency
BibRef
Canh, T.N.,
Jeon, B.,
Difference of Convolution for Deep Compressive Sensing,
ICIP19(2105-2109)
IEEE DOI
1910
compressive sensing, deep learning, difference of Gaussian,
difference of convolution
BibRef
Liu, R.,
Li, S.,
Hou, C.,
An End-to-End Multi-Scale Residual Reconstruction Network for Image
Compressive Sensing,
ICIP19(2070-2074)
IEEE DOI
1910
image compressive sensing, convolutional neural network,
reconstruction, multi-scale, end-to-end
BibRef
Cui, W.X.[Wen-Xue],
Liu, S.H.[Shao-Hui],
Zhang, S.P.[Sheng-Ping],
Liu, Y.S.[Ya-Shu],
Xu, H.Y.[He-Yao],
Gao, X.W.[Xin-Wei],
Jiang, F.[Feng],
Zhao, D.B.[De-Bin],
Classification Guided Deep Convolutional Network for Compressed
Sensing,
ICPR18(2905-2910)
IEEE DOI
1812
Image reconstruction, Feature extraction, Compressed sensing,
Adaptation models, Loss measurement, Convolution, Image coding
BibRef
Du, J.[Jiang],
Xie, X.M.[Xue-Mei],
Wang, C.Y.[Chen-Ye],
Shi, G.M.[Guang-Ming],
Color Image Reconstruction with Perceptual Compressive Sensing,
ICPR18(1512-1517)
IEEE DOI
1812
Image color analysis, Color, Image reconstruction,
Compressed sensing, Image resolution, Gray-scale, deep learning
BibRef
Zhang, J.,
Ghanem, B.,
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image
Compressive Sensing,
CVPR18(1828-1837)
IEEE DOI
1812
Transforms, Image reconstruction, Optimization,
Magnetic resonance imaging, Compressed sensing, Ear, Inverse problems
BibRef
Dave, A.,
Kumar, A.,
Vadathya,
Mitra, K.,
Compressive image recovery using recurrent generative model,
ICIP17(1702-1706)
IEEE DOI
1803
Cameras, Entropy, Image coding, Image reconstruction, Multiplexing,
Sensors, Compressive imaging, LSTMs, MAP inference, deep learning,
generative models
BibRef
Yuan, X.,
Pu, Y.,
Convolutional factor analysis inspired compressive sensing,
ICIP17(550-554)
IEEE DOI
1803
Compressed sensing, Convex functions, Convolution, Dictionaries,
Feature extraction, Image coding, Image reconstruction,
image processing
BibRef
Perdios, D.,
Besson, A.,
Rossinelli, P.,
Thiran, J.P.,
Learning the weight matrix for sparsity averaging in compressive
imaging,
ICIP17(3056-3060)
IEEE DOI
1803
Image coding, Image reconstruction, Imaging, Iterative algorithms,
Neural networks, Thresholding (Imaging), Training,
fast iterative soft thresholding
BibRef
Lohit, S.[Suhas],
Kulkarni, K.[Kuldeep],
Turaga, P.K.[Pavan K.],
Direct inference on compressive measurements using convolutional
neural networks,
ICIP16(1913-1917)
IEEE DOI
1610
Correlation
BibRef
Lohit, S.[Suhas],
Kulkarni, K.[Kuldeep],
Turaga, P.K.[Pavan K.],
Wang, J.[Jian],
Sankaranarayanan, A.C.[Aswin C.],
Reconstruction-free inference on compressive measurements,
CCD15(16-24)
IEEE DOI
1510
Correlation
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Matching Pursuits, Video Coding .