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Schmidt, U.,
Jancsary, J.[Jeremy],
Nowozin, S.[Sebastian],
Roth, S.[Stefan],
Rother, C.[Carsten],
Cascades of Regression Tree Fields for Image Restoration,
PAMI(38), No. 4, April 2016, pp. 677-689.
IEEE DOI
1603
Analytical models
BibRef
Schelten, K.[Kevin],
Nowozin, S.[Sebastian],
Jancsary, J.[Jeremy],
Rother, C.[Carsten],
Roth, S.[Stefan],
Interleaved Regression Tree Field Cascades for Blind Image
Deconvolution,
WACV15(494-501)
IEEE DOI
1503
Cameras
BibRef
Zuo, W.M.[Wang-Meng],
Ren, D.W.[Dong-Wei],
Zhang, D.,
Gu, S.H.[Shu-Hang],
Zhang, L.[Lei],
Learning Iteration-wise Generalized Shrinkage: Thresholding Operators
for Blind Deconvolution,
IP(25), No. 4, April 2016, pp. 1751-1764.
IEEE DOI
1604
Deconvolution
BibRef
Zuo, W.M.[Wang-Meng],
Ren, D.W.[Dong-Wei],
Gu, S.H.[Shu-Hang],
Lin, L.[Liang],
Zhang, L.[Lei],
Discriminative learning of iteration-wise priors for blind
deconvolution,
CVPR15(3232-3240)
IEEE DOI
1510
BibRef
Ren, W.,
Tian, J.,
Tang, Y.,
Blind Deconvolution With Nonlocal Similarity and L_0 Sparsity for
Noisy Image,
SPLetters(23), No. 4, April 2016, pp. 439-443.
IEEE DOI
1604
Cameras
BibRef
Schuler, C.J.[Christian J.],
Hirsch, M.,
Harmeling, S.[Stefan],
Scholkopf, B.[Bernhard],
Learning to Deblur,
PAMI(38), No. 7, July 2016, pp. 1439-1451.
IEEE DOI
1606
Artificial neural networks
BibRef
Xiao, L.,
Heide, F.,
Heidrich, W.,
Schölkopf, B.,
Hirsch, M.,
Discriminative Transfer Learning for General Image Restoration,
IP(27), No. 8, August 2018, pp. 4091-4104.
IEEE DOI
1806
Computational modeling, Image restoration, Noise level,
Noise reduction, Optimization, Task analysis, Training,
proximal optimization
BibRef
Burger, H.C.[Harold Christopher],
Schuler, C.J.[Christian J.],
Harmeling, S.[Stefan],
Learning How to Combine Internal and External Denoising Methods,
GCPR13(121-130).
Springer DOI
1311
BibRef
Schuler, C.J.[Christian J.],
Burger, H.C.[Harold Christopher],
Harmeling, S.[Stefan],
Scholkopf, B.[Bernhard],
A Machine Learning Approach for Non-blind Image Deconvolution,
CVPR13(1067-1074)
IEEE DOI
1309
deblurring; deconvolution; learning; neural networks
BibRef
Burger, H.C.[Harold C.],
Schuler, C.J.[Christian J.],
Harmeling, S.[Stefan],
Image denoising: Can plain neural networks compete with BM3D?,
CVPR12(2392-2399).
IEEE DOI
1208
BM3D.
See also Small Neural Networks can Denoise Image Textures Well: A Useful Complement to BM3D.
See also Block Matching 3-D Denoising, BM3D.
BibRef
Zhuang, P.X.[Pei-Xian],
Fu, X.Y.[Xue-Yang],
Huang, Y.[Yue],
Zeng, D.[Delu],
Ding, X.H.[Xing-Hao],
A novel framework method for non-blind deconvolution using subspace
images priors,
SP:IC(46), No. 1, 2016, pp. 17-28.
Elsevier DOI
1606
Non-blind deconvolution
BibRef
Zhuang, P.X.[Pei-Xian],
Fu, X.Y.[Xue-Yang],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Image enhancement using divide-and-conquer strategy,
JVCIR(45), No. 1, 2017, pp. 137-146.
Elsevier DOI
1704
Image enhancement
BibRef
Kara, F.[Fatih],
Vural, C.[Cabir],
Blind restoration and resolution enhancement of images based on complex
filtering,
SIViP(10), No. 6, June 2016, pp. 1159-1167.
WWW Link.
1608
BibRef
Liu, T.T.[Ting-Ting],
Chen, Z.Z.[Zeng-Zhao],
Liu, S.Y.[San-Yan],
Zhang, Z.L.[Zhao-Li],
Shu, J.B.[Jiang-Bo],
Blind image restoration with sparse priori regularization for passive
millimeter-wave images,
JVCIR(40, Part A), No. 1, 2016, pp. 58-66.
Elsevier DOI
1609
Millimeter wave imaging
BibRef
Wang, W.[Wei],
Ng, M.K.[Michael K.],
Convex regularized inverse filtering methods for blind image
deconvolution,
SIViP(10), No. 7, October 2016, pp. 1353-1360.
WWW Link.
1609
BibRef
Simões, M.,
Almeida, L.B.,
Bioucas-Dias, J.,
Chanussot, J.,
A Framework for Fast Image Deconvolution With Incomplete Observations,
IP(25), No. 11, November 2016, pp. 5266-5280.
IEEE DOI
1610
Boundary conditions
BibRef
Song, Y.Y.[Ying-Ying],
Brie, D.[David],
Djermoune, E.H.[El-Hadi],
Henrot, S.[Simon],
Regularization Parameter Estimation for Non-Negative Hyperspectral
Image Deconvolution,
IP(25), No. 11, November 2016, pp. 5316-5330.
IEEE DOI
1610
Convolution.
BibRef
Wang, L.,
Chi, Y.,
Blind Deconvolution From Multiple Sparse Inputs,
SPLetters(23), No. 10, October 2016, pp. 1384-1388.
IEEE DOI
1610
convex programming
BibRef
Qu, Y.[Ying],
Koschan, A.[Andreas],
Abidi, M.[Mongi],
Robust penalty-weighted deblurring via kernel adaption using single
image,
JVCIR(41), No. 1, 2016, pp. 109-122.
Elsevier DOI
1612
Blind deconvolution
BibRef
Badri, H.[Hicham],
Yahia, H.[Hussein],
Aboutajdine, D.,
Low-Rankness Transfer for Realistic Denoising,
IP(25), No. 12, December 2016, pp. 5768-5779.
IEEE DOI
1612
Gaussian noise
BibRef
Badri, H.[Hicham],
Yahia, H.[Hussein],
Handling noise in image deconvolution with local/non-local priors,
ICIP14(2644-2648)
IEEE DOI
1502
Convolution
BibRef
Kotera, J.[Jan],
Šmídl, V.,
Šroubek, F.[Filip],
Blind Deconvolution With Model Discrepancies,
IP(26), No. 5, May 2017, pp. 2533-2544.
IEEE DOI
1704
Bayes methods
BibRef
Ševcík, J.,
Šmídl, V.,
Šroubek, F.,
An Adaptive Correlated Image Prior for Image Restoration Problems,
SPLetters(25), No. 7, July 2018, pp. 1024-1028.
IEEE DOI
1807
Bayes methods, image resolution, image restoration,
variational techniques, Bayesian interpretation,
variational Bayes
BibRef
Kotera, J.[Jan],
Šroubek, F.[Filip],
Milanfar, P.[Peyman],
Blind Deconvolution Using Alternating Maximum a Posteriori Estimation
with Heavy-Tailed Priors,
CAIP13(II:59-66).
Springer DOI
1311
BibRef
Yang, C.X.[Chang-Xing],
Shao, W.Z.[Wen-Ze],
Huang, L.L.[Li-Li],
Boosting normalized sparsity regularization for blind image
deconvolution,
SIViP(11), No. 4, May 2017, pp. 681-688.
WWW Link.
1704
BibRef
Sonogashira, M.[Motoharu],
Iiyama, M.[Masaaki],
Minoh, M.[Michihiko],
Shift-Variant Blind Deconvolution Using a Field of Kernels,
IEICE(E100-D), No. 9, September 2017, pp. 1971-1983.
WWW Link.
1709
BibRef
Abboud, F.[Feriel],
Chouzenoux, E.[Emilie],
Pesquet, J.C.[Jean-Christophe],
Chenot, J.H.[Jean-Hugues],
Laborelli, L.[Louis],
Dual Block-Coordinate Forward-Backward Algorithm with Application to
Deconvolution and Deinterlacing of Video Sequences,
JMIV(59), No. 3, November 2017, pp. 415-431.
Springer DOI
1710
BibRef
Earlier:
A dual block coordinate proximal algorithm with application to
deconvolution of interlaced video sequences,
ICIP15(4917-4921)
IEEE DOI
1512
Proximity operator
BibRef
Abboud, F.[Feriel],
Chouzenoux, É.[Émilie],
Pesquet, J.C.[Jean-Christophe],
Chenot, J.H.[Jean-Hugues],
Laborelli, L.[Louis],
An alternating proximal approach for blind video deconvolution,
SP:IC(70), 2019, pp. 21-36.
Elsevier DOI
1812
Blind deconvolution, Video processing, Regularization,
Nonconvex optimization, Proximal algorithms
BibRef
Yuan, S.,
Wang, S.,
Ma, M.,
Ji, Y.,
Deng, L.,
Sparse Bayesian Learning-Based Time-Variant Deconvolution,
GeoRS(55), No. 11, November 2017, pp. 6182-6194.
IEEE DOI
1711
Attenuation, Bayes methods, Convolution, Deconvolution,
Time-frequency analysis, Attenuation, Bayesian framework,
inverse problems, sparse Bayesian learning (SBL),
BibRef
Li, J.,
Luisier, F.,
Blu, T.,
PURE-LET Image Deconvolution,
IP(27), No. 1, January 2018, pp. 92-105.
IEEE DOI
1712
BibRef
Earlier:
Deconvolution of poissonian images with the PURE-LET approach,
ICIP16(2708-2712)
IEEE DOI
1610
Gaussian noise, Haar transforms, Wiener filters, deconvolution,
image restoration, image segmentation, mean square error methods,
unbiased risk estimate
BibRef
Ren, D.,
Zuo, W.,
Zhang, D.,
Xu, J.,
Zhang, L.,
Partial Deconvolution With Inaccurate Blur Kernel,
IP(27), No. 1, January 2018, pp. 511-524.
IEEE DOI
1712
Computational modeling, Deconvolution, Estimation error, Kernel,
Robustness, E-M algorithm, Image deblurring, blind deconvolution, blur kernel estimation
BibRef
Gabarda, S.[Salvador],
Cristóbal, G.[Gabriel],
Goel, N.[Navdeep],
Anisotropic blind image quality assessment: Survey and analysis with
current methods,
JVCIR(52), 2018, pp. 101-105.
Elsevier DOI
1804
Image quality assessment, Pseudo-Wigner distribution,
Rényi entropy, Anisotropy, Correlation, Mean opinion scores
BibRef
Carrasquinha, E.[Eunice],
Amado, C.[Conceição],
Pires, A.M.[Ana M.],
Oliveira, L.[Lina],
Image reconstruction based on circulant matrices,
SP:IC(63), 2018, pp. 72-80.
Elsevier DOI
1804
PCA, Signal processing, Image reconstruction,
Circulant matrices, Toeplitz matrices
BibRef
Wang, R.,
Tao, D.,
Training Very Deep CNNs for General Non-Blind Deconvolution,
IP(27), No. 6, June 2018, pp. 2897-2910.
IEEE DOI
1804
Computational modeling, Convolution, Deconvolution,
Image restoration, Kernel, Task analysis, Training, Deep CNNs,
residual learning
BibRef
Han, J.,
Song, K.S.,
Kim, J.,
Kang, M.G.,
Permuted Coordinate-Wise Optimizations Applied to Lp-Regularized
Image Deconvolution,
IP(27), No. 7, July 2018, pp. 3556-3570.
IEEE DOI
1805
Cost function, Deconvolution, Estimation, Iterative methods, Kernel,
Linear programming, Lp-norm, Non-blind deconvolution,
total variation
BibRef
Lamash, Y.[Yechiel],
Algorithm and Constraints for Exact Non-blind Deconvolution,
JMIV(60), No. 5, June 2018, pp. 692-706.
WWW Link.
1806
BibRef
Winkler, J.R.[Joab R.],
Halawani, H.[Hanan],
The Sylvester and Bézout Resultant Matrices for Blind Image
Deconvolution,
JMIV(60), No. 8, October 2018, pp. 1284-1305.
Springer DOI
1810
BibRef
Huang, W.,
Hand, P.,
Blind Deconvolution by a Steepest Descent Algorithm on a Quotient
Manifold,
SIIMS(11), No. 4, 2018, pp. 2757-2785.
DOI Link
1901
BibRef
Gong, D.[Dong],
Tan, M.K.[Ming-Kui],
Shi, Q.F.[Qin-Feng],
van den Hengel, A.J.[Anton J.],
Zhang, Y.N.[Yan-Ning],
MPTV: Matching Pursuit-Based Total Variation Minimization for Image
Deconvolution,
IP(28), No. 4, April 2019, pp. 1851-1865.
IEEE DOI
1901
BibRef
Earlier: A1, A2, A5, A4, A3:
Self-Paced Kernel Estimation for Robust Blind Image Deblurring,
ICCV17(1670-1679)
IEEE DOI
1802
BibRef
Earlier: A1, A2, A5, A4, A3:
Blind Image Deconvolution by Automatic Gradient Activation,
CVPR16(1827-1836)
IEEE DOI
1612
convex programming, deconvolution,
gradient methods, image denoising, image reconstruction,
convex programming.
estimation theory, image enhancement,
image restoration, learning (artificial intelligence),
Robustness
BibRef
Boudjenouia, F.[Fouad],
Abed-Meraim, K.[Karim],
Chetouani, A.[Aladine],
Jennane, R.[Rachid],
Robust, blind multichannel image identification and restoration using
stack decoder,
IET-IPR(13), No. 3, February 2019, pp. 475-482.
DOI Link
1903
BibRef
Abbass, M.Y.,
Kim, H.[HyungWon],
Abdelwahab, S.A.[Safey A.],
Haggag, S.S.,
El-Rabaie, E.S.M.[El-Sayed M.],
Dessouky, M.I.[Moawad I.],
El-Samie, F.E.A.[Fathi E. Abd],
Image deconvolution using homomorphic technique,
SIViP(13), No. 4, June 2019, pp. 703-709.
Springer DOI
1906
BibRef
Xue, F.[Feng],
Liu, J.[Jiaqi],
Ai, X.[Xia],
Parametric PSF estimation based on predicted-SURE with l1-penalized
sparse deconvolution,
SIViP(13), No. 4, June 2019, pp. 635-642.
WWW Link.
1906
BibRef
Siadat, M.,
Aghazadeh, N.,
Akbarifard, F.,
Brismar, H.,
Öktem, O.,
Joint Image Deconvolution and Separation Using Mixed Dictionaries,
IP(28), No. 8, August 2019, pp. 3936-3945.
IEEE DOI
1907
deconvolution, image reconstruction, image representation,
iterative methods, synthetic data,
wavelets
BibRef
Hajmohammadi, S.[Solmaz],
Nooshabadi, S.[Saeid],
Archer, G.E.[Glen E.],
Bos, J.P.,
Struther, A.[Allan],
Parallel hybrid bispectrum-multi-frame blind deconvolution image
reconstruction technique,
RealTimeIP(16), No. 4, August 2019, pp. 919-929.
Springer DOI
1908
BibRef
Güngör, A.,
Kar, O.F.,
A Transform Learning Based Deconvolution Technique with
Super-Resolution and Microscanning Applications,
ICIP19(2159-2163)
IEEE DOI
1910
Deconvolution, Sparsity, Transform Learning, Computational Imaging,
Super-Resolution
BibRef
Ancora, D.,
Bassi, A.,
Deconvolved Image Restoration From Auto-Correlations,
IP(30), 2021, pp. 1332-1341.
IEEE DOI
2012
Image reconstruction, Kernel, Imaging, Correlation,
Extraterrestrial measurements, Deconvolution, Convolution,
inverse problem
BibRef
Zhang, Y.,
Lau, Y.,
Kuo, H.W.,
Cheung, S.,
Pasupathy, A.,
Wright, J.,
On the Global Geometry of Sphere-Constrained Sparse Blind
Deconvolution,
PAMI(43), No. 3, March 2021, pp. 999-1008.
IEEE DOI
2102
BibRef
Earlier:
CVPR17(4381-4389)
IEEE DOI
1711
Convolution, Deconvolution, Kernel, Geometry, Image restoration,
Optimization, Microscopy, Image deblurring, blind deconvolution,
nonconvex optimization.
Convolution, Deconvolution, Geometry, Image restoration, Kernel,
Microscopy, Optimization
BibRef
Gao, W.Z.[Wei-Zhe],
Xu, X.B.[Xue-Bin],
Yang, Y.K.[Yi-Kang],
Zhang, Z.G.[Zhi-Guang],
Non-iterative blind deconvolution algorithm based on power-law
distribution,
IET-IPR(14), No. 17, 24 December 2020, pp. 4499-4506.
DOI Link
2104
BibRef
Tao, S.Y.[Shu-Yin],
Dong, W.[Wende],
Xu, J.[Jian],
Lu, J.F.[Jian-Feng],
Xu, G.[Guili],
Chen, Y.T.[Yue-Ting],
An adaptive two phase blind image deconvolution algorithm for an
iterative regularization model,
JVCIR(81), 2021, pp. 103370.
Elsevier DOI
2112
Blind image deconvolution, -norm gradient regularization, TV regularization
BibRef
Liu, J.[Jing],
Tan, J.Q.[Jie-Qing],
Zhang, L.[Li],
Ge, X.Y.[Xian-Yu],
Hu, D.D.[Dan-Dan],
Blind deblurring with patch-wise second-order gradient prior,
SP:IC(107), 2022, pp. 116781.
Elsevier DOI
2208
Motion deblurring, Second-order gradient, Kernel similarity,
Blind deconvolution, Local image patches
BibRef
Liu, J.[Jing],
Tan, J.Q.[Jie-Qing],
Ge, X.Y.[Xian-Yu],
Hu, D.D.[Dan-Dan],
He, L.[Lei],
Blind deblurring with fractional-order calculus and local minimal
pixel prior,
JVCIR(89), 2022, pp. 103645.
Elsevier DOI
2212
Motion deblurring, Deconvolution, Kernel estimation,
Fractional-Order calculus Theory, Local Minimal Pixel Prior
BibRef
Wang, W.X.[Wei-Xi],
Li, J.[Ji],
Ji, H.[Hui],
L_1-Norm Regularization for Short-and-Sparse Blind Deconvolution:
Point Source Separability and Region Selection,
SIIMS(15), No. 3, 2022, pp. 1345-1372.
DOI Link
2208
BibRef
Jin, Y.[Yan],
Jiang, Z.W.[Zhi-Wei],
Xue, Z.Z.[Zhi-Zhong],
Hu, Y.B.[Yi-Biao],
Image blind restoration based on degradation representation network,
JVCIR(87), 2022, pp. 103564.
Elsevier DOI
2208
Deep learning, Contrastive learning, Image restoration,
Convolution neural network
BibRef
Traullé, B.[Benjamin],
Bidon, S.[Stéphanie],
Roque, D.[Damien],
A Reversible Jump MCMC in Bayesian Blind Deconvolution With a
Spherical Prior,
SPLetters(29), 2022, pp. 2372-2376.
IEEE DOI
2212
Bayes methods, Signal processing algorithms, Deconvolution,
Space exploration, Monte Carlo methods, Markov processes,
von Mises-Fisher prior
BibRef
Chen, Z.J.[Zhuo-Jie],
Yao, X.[Xin],
Xu, Y.[Yong],
Wang, J.[Junle],
Quan, Y.H.[Yu-Hui],
Unsupervised knowledge transfer for nonblind image deconvolution,
PRL(164), 2022, pp. 232-238.
Elsevier DOI
2212
Nonblind image deconvolution, Deep knowledge transfer,
Model adaption, Image recovery
BibRef
Huang, Y.S.[Yun-Shi],
Chouzenoux, E.[Emilie],
Pesquet, J.C.[Jean-Christophe],
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution,
IP(32), 2023, pp. 430-445.
IEEE DOI
2301
Bayes methods, Kernel, Deconvolution, Image restoration, Training,
Deep learning, Tuning, Variational Bayesian approach
BibRef
Zhao, Y.[Yang],
Jia, W.[Wei],
Chen, Y.[Yuan],
Wang, R.G.[Rong-Gang],
Fast Blind Decontouring Network,
CirSysVideo(33), No. 2, February 2023, pp. 478-490.
IEEE DOI
2302
Task analysis, Image coding, Degradation, Automobiles, Training,
Image restoration, Frequency division multiplexing, Decontouring,
flat region detection
BibRef
Huo, D.[Dong],
Masoumzadeh, A.[Abbas],
Kushol, R.[Rafsanjany],
Yang, Y.H.[Yee-Hong],
Blind Image Deconvolution Using Variational Deep Image Prior,
PAMI(45), No. 10, October 2023, pp. 11472-11483.
IEEE DOI
2310
BibRef
Lyu, Z.Y.[Zhi-Yu],
Chen, Y.[Yan],
Sun, H.J.[Hao-Jun],
Hou, Y.M.[Yi-Min],
A dual fusion deep convolutional network for blind universal image
denoising,
SP:IC(120), 2024, pp. 117077.
Elsevier DOI
2312
Blind image denoising, Deep convolutional neural network,
Non-subsampled shearlet transform, Edge preservation
BibRef
Qiao, Y.J.[Yuan-Jian],
Shao, M.W.[Ming-Wen],
Wang, L.Q.[Lei-Quan],
Zuo, W.M.[Wang-Meng],
Learning Depth-Density Priors for Fourier-Based Unpaired Image
Restoration,
CirSysVideo(34), No. 4, April 2024, pp. 2604-2618.
IEEE DOI
2404
Degradation, Image restoration, Rain, Visualization,
Atmospheric modeling, Rendering (computer graphics),
spatial-frequency interaction
BibRef
Mbakam, C.K.[Charlesquin Kemajou],
Pereyra, M.[Marcelo],
Giovannelli, J.F.[Jean-Francois],
Marginal Likelihood Estimation in Semiblind Image Deconvolution:
A Stochastic Approximation Approach,
SIIMS(17), No. 2, 2024, pp. 1206-1254.
DOI Link
2407
BibRef
Liu, Y.Q.[Ying-Qi],
He, J.W.[Jing-Wen],
Liu, Y.H.[Yi-Hao],
Lin, X.[Xinqi],
Yu, F.[Fanghua],
Hu, J.[Jinfan],
Qiao, Y.[Yu],
Dong, C.[Chao],
AdaptBIR: Adaptive Blind Image Restoration with latent diffusion
prior for higher fidelity,
PR(155), 2024, pp. 110659.
Elsevier DOI
2408
Image restoration, Diffusion model, Adaptive adjustment
BibRef
Oppenheim, A.V.[Alan V.],
Schafer, R.W.[Ronald W.],
Ward, J.[James],
Efficient Deconvolution With the Discrete Fourier Transform,
SPMag(41), No. 4, July 2024, pp. 76-83.
IEEE DOI
2410
BibRef
Ji, Y.X.[Yi-Xing],
Kong, S.J.[Sheng-Jiang],
Wang, W.W.[Wei-Wei],
Jia, X.X.[Xi-Xi],
Feng, X.C.[Xiang-Chu],
Iterative decoupling deconvolution network for image restoration,
JVCIR(104), 2024, pp. 104288.
Elsevier DOI
2411
Deep convolutional neural network, Deep unfolding neural network,
Image restoration
BibRef
Laribi, G.[Ghada],
Welk, M.[Martin],
Towards Quality Assessment of Blind Deconvolution with Shift
Compensation,
ICPR22(421-427)
IEEE DOI
2212
Deconvolution, Systematics, Measurement uncertainty, Estimation,
Displacement measurement, Time measurement, Image restoration, Fourier method
BibRef
Kuo, P.H.[Pin-Hung],
Pan, J.S.[Jin-Shan],
Chien, S.Y.[Shao-Yi],
Yang, M.H.[Ming-Hsuan],
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution,
ECCV22(XIX:217-234).
Springer DOI
2211
BibRef
Sanders, T.[Toby],
Blind Deconvolution Using the Sure-Blur Criterion and Linear PSF
Expansions,
ICIP22(3475-3479)
IEEE DOI
2211
Deconvolution, Filtering, Gaussian distribution, Minimization,
Optimization, Blind deconvolution, SURE, Wiener filtering
BibRef
Kotera, J.[Jan],
Šroubek, F.[Filip],
Šmidl, V.[Václav],
Improving Neural Blind Deconvolution,
ICIP21(1954-1958)
IEEE DOI
2201
Learning systems, Deconvolution, Neural networks,
Image restoration, Convolutional neural networks, Convergence,
multiscale
BibRef
Hu, X.[Xiaowan],
Ma, R.[Ruijun],
Liu, Z.H.[Zhi-Hong],
Cai, Y.H.[Yuan-Hao],
Zhao, X.L.[Xiao-Le],
Zhang, Y.[Yulun],
Wang, H.Q.[Hao-Qian],
Pseudo 3D Auto-Correlation Network for Real Image Denoising,
CVPR21(16170-16179)
IEEE DOI
2111
Deep learning, Visualization,
Convolution, Stacking, Memory management, Feature extraction
BibRef
Soh, J.W.[Jae Woong],
Cho, N.I.[Nam Ik],
Deep Universal Blind Image Denoising,
ICPR21(747-754)
IEEE DOI
2105
Noise reduction, Tools,
Random variables, Bayes methods
BibRef
Ko, H.C.[Hung-Chih],
Chang, J.Y.[Je-Yuan],
Ding, J.J.[Jian-Jiun],
Deep Priors Inside an Unrolled and Adaptive Deconvolution Model,
ACCV20(II:371-388).
Springer DOI
2103
BibRef
Unni, V.S.,
Chaudhury, K.N.,
Kernel Regularization for Image Restoration,
ICIP20(943-947)
IEEE DOI
2011
Image restoration, Kernel, Eigenvalues and eigenfunctions,
Noise reduction, Image resolution, Convex functions, Deconvolution,
kernel denoiser
BibRef
Ren, D.,
Zhang, K.,
Wang, Q.,
Hu, Q.,
Zuo, W.,
Neural Blind Deconvolution Using Deep Priors,
CVPR20(3338-3347)
IEEE DOI
2008
Kernel, Deconvolution, Optimization, Electronics packaging,
Machine learning, Estimation, Convolution
BibRef
Zhao, H.,
Shao, W.,
Bao, B.,
Li, H.,
A Simple and Robust Deep Convolutional Approach to Blind Image
Denoising,
CLI19(3943-3951)
IEEE DOI
2004
Code, Convolutional Networks.
WWW Link. AWGN, convolutional neural nets, feature extraction,
Gaussian noise, image denoising,
noise estimation
BibRef
Wang, Z.,
Wang, Z.,
Li, Q.,
Bilen, H.,
Image Deconvolution with Deep Image and Kernel Priors,
RLQ19(980-989)
IEEE DOI
2004
deconvolution, image reconstruction, image restoration,
inverse problems, learning (artificial intelligence),
Learning Free
BibRef
Aljadaany, R.[Raied],
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Douglas-Rachford Networks: Learning Both the Image Prior and Data
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IEEE DOI
2002
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Guo, S.[Shi],
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2002
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ICIP19(4669-4673)
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1910
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Yao, L.S.,
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Understanding Kernel Size in Blind Deconvolution,
WACV19(2068-2076)
IEEE DOI
1904
deconvolution, image restoration, optimisation,
blind deconvolution methods, support domain,
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Modeling Realistic Degradations in Non-Blind Deconvolution,
ICIP18(978-982)
IEEE DOI
1809
Quantization (signal), Deconvolution, Degradation,
Image restoration, Cameras, Kernel, Stochastic processes,
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ICCV17(1799-1808)
IEEE DOI
1802
deconvolution, image denoising, image restoration,
image segmentation, inverse problems,
Training
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Shaham, T.R.[Tamar Rott],
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Deformation Aware Image Compression,
CVPR18(2453-2462)
IEEE DOI
1812
Image coding, Strain, Transform coding, Codecs,
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Blau, Y.,
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The Perception-Distortion Tradeoff,
CVPR18(6228-6237)
IEEE DOI
1812
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ECCV16(VI: 136-153).
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1611
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ICIVC16(15-18)
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Bayes methods
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ICIP16(2713-2717)
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1610
Convolution
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Satellite Image Restoration by Applying the Genetic Approach to the
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CGiV16(57-61)
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deconvolution
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ICIP15(784-788)
IEEE DOI
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Image restoration
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ICIP15(4892-4896)
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1512
Bayesian methods
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1511
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Deconvolution for slowly time-varying systems 3D cases,
IPTA12(121-126)
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Kalman filters
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ICIP14(1783-1787)
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Approximation methods
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ICIP14(4276-4279)
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Deconvolution
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Blind equalizers
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ICIP14(4537-4541)
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Approximation methods
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ICIP14(1347-1351)
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Compressed sensing
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Split Bregman algorithms for sparse/joint-sparse and low-rank
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1502
Algorithm design and analysis
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ICIP13(877-881)
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1402
Cameras
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ICIP13(1391-1393)
IEEE DOI
1402
Blind deconvolution;image reconstruction;robust estimation
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Morin, R.[Renaud],
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Semi-blind deconvolution for resolution enhancement in ultrasound
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ICIP13(1413-1417)
IEEE DOI
1402
Resolution enhancement
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Shrinkage Fields for Effective Image Restoration,
CVPR14(2774-2781)
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Award, CVPR, Student.
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1201
Deblurring. Non-linear blurring
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ICIP11(2709-2712).
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1201
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1008
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ICIP08(673-676).
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ICIP08(505-508).
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0810
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Jain, V.[Viren],
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0312
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ICIP99(I:198-200).
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Digital processing of skylab X-ray images of the solar corona,
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Space-Variant Deconvolution for Synthetic Aperture Imaging
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ICIP94(II: 163-167).
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ICIP94(III: 192-196).
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9411
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Molina, R.,
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On the Bayesian deconvolution of planets,
ICPR92(III:147-150).
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9208
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Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Color, Multispectral, Multi-Channel Restoration .