Zhou, Y.T.,
Chellappa, R., and
Jenkins, B.K.,
Image Restoration Using a Neural Network,
ASSP(36), July 1988, pp. 1141-1151.
BibRef
8807
Figueiredo, M.A.T.,
Leitao, J.M.N.,
Sequential and Parallel Image Restoration:
Neural Network Implementations,
IP(3), No. 6, November 1994, pp. 789-801.
IEEE DOI
0402
BibRef
And:
Adaptive discontinuity location in image restoration,
ICIP94(II: 665-669).
IEEE DOI
9411
BibRef
Wong, H.S.[Hau-San],
Guan, L.[Ling],
Adaptive Regularization in Image Restoration Using a
Model Based Neural Network,
OptEng(36), No. 12, December 1997, pp. 3297-3308.
9801
BibRef
Wong, H.S.[Hau-San],
Guan, L.[Ling],
Adaptive Regularization in Image Restoration by Unsupervised Learning,
JEI(7), No. 1, January 1998, pp. 211-221.
9807
BibRef
And:
A Fuzzy Model-based Neural Network for Adaptive Regularization in Image
Restoration,
ICIP99(I:391-395).
IEEE DOI
BibRef
Leitao, J.M.N.,
Figueiredo, M.A.T.,
Absolute Phase Image-Reconstruction:
A Stochastic Nonlinear Filtering Approach,
IP(7), No. 6, June 1998, pp. 868-882.
IEEE DOI
9806
BibRef
Wang, Y.,
Wahl, F.M.,
Multiobjective Neural Network for Image Reconstruction,
VISP(144), No. 4, August 1997, pp. 233-236.
9806
BibRef
Sun, Y.[Yi],
Li, J.G.[Jie-Gu],
Yu, S.Y.[Song-Yu],
Improvement on performance of modified Hopfield neural network for
image restoration,
IP(4), No. 5, May 1995, pp. 688-692.
IEEE DOI
0402
BibRef
Wang, Y.M.[Yuan-Mei],
Neural Network Approach to Image Reconstruction from Projections,
IJIST(9), No. 5, 1999, pp. 381-387.
BibRef
9900
Woo, W.L.,
Khor, L.C.,
Blind restoration of nonlinearly mixed signals using multilayer
polynomial neural network,
VISP(151), No. 1, February 2004, pp. 51-61.
IEEE Abstract.
0403
BibRef
Woo, W.L.,
Dlay, S.S.,
Regularised nonlinear blind signal separation using sparsely connected
network,
VISP(152), No. 1, February 2005, pp. 61-73.
IEEE Abstract.
0501
BibRef
Woo, W.L.,
Dlay, S.S.,
Nonlinear blind source separation using a hybrid RBF-FMLP network,
VISP(152), No. 2, April 2005, pp. 173-183.
DOI Link
0510
BibRef
Khor, L.C.,
Woo, W.L.,
Dlay, S.S.,
Nonlinear blind signal separation with intelligent controlled learning,
VISP(152), No. 3, June 2005, pp. 297-306.
DOI Link
0510
BibRef
Wei, C.,
Woo, W.L.,
Dlay, S.S.,
Khor, L.C.,
Maximum a posteriori-based approach to blind nonlinear underdetermined
mixture,
VISP(153), No. 4, August 2006, pp. 419-430.
WWW Link.
0705
BibRef
Bao, P.[Paul],
Wang, D.H.[Dian-Hui],
An Edge-Preserving Image Reconstruction Using Neural Network,
JMIV(14), No. 2, March 2001, pp. 117-130.
DOI Link
0106
BibRef
Lyu, G.H.[Guo-Hao],
Yin, H.[Hui],
Yu, X.Y.[Xin-Yan],
Luo, S.W.[Si-Wei],
A Local Characteristic Image Restoration Based on Convolutional Neural
Network,
IEICE(E99-D), No. 8, August 2016, pp. 2190-2193.
WWW Link.
1608
BibRef
Zhang, K.[Kai],
Zuo, W.M.[Wang-Meng],
Chen, Y.J.[Yun-Jin],
Meng, D.Y.[De-Yu],
Zhang, L.[Lei],
Beyond a Gaussian Denoiser:
Residual Learning of Deep CNN for Image Denoising,
IP(26), No. 7, July 2017, pp. 3142-3155.
IEEE DOI
1706
Computational modeling, Image denoising, Neural networks,
Noise level, Noise reduction, Training, Transform coding,
Image denoising, batch normalization,
convolutional neural networks, residual, learning
BibRef
Zhang, K.[Kai],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image
Denoising,
IP(27), No. 9, September 2018, pp. 4608-4622.
IEEE DOI
1807
image denoising, image sampling,
learning (artificial intelligence), neural nets,
spatially variant noise
See also Analysis and Implementation of the FFDNet Image Denoising Method, An.
BibRef
Zhang, K.[Kai],
Li, Y.[Yawei],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
Plug-and-Play Image Restoration With Deep Denoiser Prior,
PAMI(44), No. 10, October 2022, pp. 6360-6376.
IEEE DOI
2209
Image restoration, Task analysis, Noise reduction, Noise level,
Learning systems, Training, Optimization, Denoiser prior,
plug-and-play
BibRef
Zhang, K.[Kai],
Zuo, W.M.[Wang-Meng],
Gu, S.,
Zhang, L.[Lei],
Learning Deep CNN Denoiser Prior for Image Restoration,
CVPR17(2808-2817)
IEEE DOI
1711
Image restoration, Inverse problems, Learning systems,
Noise reduction, Optimization, methods
BibRef
Zhang, F.[Fu],
Cai, N.[Nian],
Wu, J.X.[Ji-Xiu],
Cen, G.D.[Guan-Dong],
Wang, H.[Han],
Chen, X.D.[Xin-Du],
Image Denoising Method Based on a Deep Convolution Neural Network,
IET-IPR(12), No. 4, April 2018, pp. 485-493.
DOI Link
1804
BibRef
Yin, J.,
Chen, B.,
Li, Y.,
Highly Accurate Image Reconstruction for Multimodal Noise Suppression
Using Semisupervised Learning on Big Data,
MultMed(20), No. 11, November 2018, pp. 3045-3056.
IEEE DOI
1810
Image reconstruction, Noise measurement, Streaming media,
Cost function, Semisupervised learning, Big Data, Imaging,
semisupervised learning
BibRef
Tassano, M.[Matias],
Delon, J.[Julie],
Veit, T.[Thomas],
An Analysis and Implementation of the FFDNet Image Denoising Method,
IPOL(9), 2019, pp. 1-25.
DOI Link
1901
Code, Noise Removal.
See also FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising.
BibRef
Xu, W.J.[Wen-Jia],
Xu, G.L.[Guang-Luan],
Wang, Y.[Yang],
Sun, X.[Xian],
Lin, D.[Daoyu],
Wu, Y.R.[Yi-Rong],
Deep Memory Connected Neural Network for Optical Remote Sensing Image
Restoration,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link
1901
BibRef
Cho, S.I.,
Kang, S.,
Gradient Prior-Aided CNN Denoiser With Separable Convolution-Based
Optimization of Feature Dimension,
MultMed(21), No. 2, February 2019, pp. 484-493.
IEEE DOI
1902
Convolution, Noise reduction, Image denoising, Feature extraction,
Training, Noise measurement, Indexes, Image denoising,
image noise
BibRef
Xue, H.Z.[Hong-Zhi],
Cui, H.W.[Hong-Wei],
Research on image restoration algorithms based on BP neural network,
JVCIR(59), 2019, pp. 204-209.
Elsevier DOI
1903
Image restoration, Image processing, Image denoising, BP neural network
BibRef
Shi, W.Z.[Wu-Zhen],
Jiang, F.[Feng],
Zhang, S.P.[Sheng-Ping],
Wang, R.[Rui],
Zhao, D.B.[De-Bin],
Zhou, H.Y.[Hui-Yu],
Hierarchical residual learning for image denoising,
SP:IC(76), 2019, pp. 243-251.
Elsevier DOI
1906
Image denoising, Convolutional neural network,
Residual learning, Hierarchical residual learning, Multi-scale information
BibRef
Shin, S.Y.[Soo-Yeon],
Kim, D.M.[Dong-Myung],
Suh, J.W.[Jae-Won],
Image Denoiser Using Convolutional Neural Network with Deconvolution
and Modified Residual Network,
IEICE(E102-D), No. 8, August 2019, pp. 1598-1601.
WWW Link.
1908
BibRef
Dong, W.S.[Wei-Sheng],
Wang, P.Y.[Pei-Yao],
Yin, W.T.[Wo-Tao],
Shi, G.M.[Guang-Ming],
Wu, F.F.[Fang-Fang],
Lu, X.T.[Xiao-Tong],
Denoising Prior Driven Deep Neural Network for Image Restoration,
PAMI(41), No. 10, October 2019, pp. 2305-2318.
IEEE DOI
1909
Task analysis, Noise reduction, Image restoration, Optimization,
Image resolution, Neural networks, Iterative algorithms,
image restoration
BibRef
Wang, F.,
Huang, H.,
Liu, J.,
Variational-Based Mixed Noise Removal With CNN Deep Learning
Regularization,
IP(29), 2020, pp. 1246-1258.
IEEE DOI
1911
Deep learning, Noise reduction, Image restoration,
Learning systems, TV, Data models, Deep learning, CNN, regularization,
image restoration
BibRef
Jiang, Y.,
Li, H.,
Rangaswamy, M.,
Deep Learning Denoising Based Line Spectral Estimation,
SPLetters(26), No. 11, November 2019, pp. 1573-1577.
IEEE DOI
1911
convolutional neural nets, learning (artificial intelligence),
minimisation, signal denoising, deep learning
BibRef
Thakur, R.S.[Rini Smita],
Yadav, R.N.[Ram Narayan],
Gupta, L.[Lalita],
State-of-art analysis of image denoising methods using convolutional
neural networks,
IET-IPR(13), No. 13, November 2019, pp. 2367-2380.
DOI Link
1911
BibRef
Qu, C.,
Moiseikin, M.,
Voth, S.,
Beyerer, J.[Jurgen],
CNN-based Image Denoising for Outdoor Active Stereo,
MVA19(1-6)
DOI Link
1911
convolutional neural nets, image denoising,
image matching, image reconstruction, image texture,
Adaptive equalizers
BibRef
Somasundaran, B.V.[Biju Venkadath],
Soundararajan, R.[Rajiv],
Biswas, S.[Soma],
Robust image retrieval by cascading a deep quality assessment network,
SP:IC(80), 2020, pp. 115652.
Elsevier DOI
1912
BibRef
Earlier:
Image Denoising for Image Retrieval by Cascading a Deep Quality
Assessment Network,
ICIP18(525-529)
IEEE DOI
1809
Image enhancement, Image quality assessment,
Deep convolutional neural network, Denoising, Image retrieval.
Noise reduction, Image denoising, Image quality,
Noise measurement, Training.
BibRef
Guo, Y.C.[Yong-Cun],
Jia, X.F.[Xiao-Fen],
Zhao, B.T.[Bai-Ting],
Chai, H.R.[Hua-Rong],
Huang, Y.R.[You-Rui],
Multifeature extracting CNN with concatenation for image denoising,
SP:IC(81), 2020, pp. 115690.
Elsevier DOI
1912
BibRef
Ma, R.J.[Rui-Jun],
Hu, H.F.[Hai-Feng],
Xing, S.L.[Song-Long],
Li, Z.M.[Zheng-Ming],
Efficient and Fast Real-World Noisy Image Denoising by Combining
Pyramid Neural Network and Two-Pathway Unscented Kalman Filter,
IP(29), 2020, pp. 3927-3940.
IEEE DOI
2002
Image prior, real-world noisy image denoising, pyramid network,
two-pathway unscented Kalman filter
BibRef
Ma, R.J.[Rui-Jun],
Li, S.Y.[Shu-Yi],
Zhang, B.[Bob],
Hu, H.F.[Hai-Feng],
Meta PID Attention Network for Flexible and Efficient Real-World
Noisy Image Denoising,
IP(31), 2022, pp. 2053-2066.
IEEE DOI
2203
Noise measurement, Noise reduction, Image denoising, Cameras,
PD control, PI control, Adaptation models,
attention network
BibRef
Spigler, G.[Giacomo],
Denoising Autoencoders for Overgeneralization in Neural Networks,
PAMI(42), No. 4, April 2020, pp. 998-1004.
IEEE DOI
2003
Training, Neural networks, Computational modeling, Noise reduction,
Data models, Support vector machines, Mars, Overgeneralization,
neural networks
BibRef
Yang, X.,
Xu, Y.,
Quan, Y.,
Ji, H.,
Image Denoising via Sequential Ensemble Learning,
IP(29), 2020, pp. 5038-5049.
IEEE DOI
2003
Image denoising, Noise reduction, Machine learning, Transforms,
Noise measurement, Iterative methods, Manifolds, Image denoising,
ensemble denoiser
BibRef
Jin, Z.,
Iqbal, M.Z.,
Bobkov, D.,
Zou, W.,
Li, X.,
Steinbach, E.,
A Flexible Deep CNN Framework for Image Restoration,
MultMed(22), No. 4, April 2020, pp. 1055-1068.
IEEE DOI
2004
Training, Image restoration, Image coding, Automobiles,
Task analysis, Transform coding, Image denoising, residual learning
BibRef
Wen, B.,
Li, Y.,
Bresler, Y.,
Image Recovery via Transform Learning and Low-Rank Modeling:
The Power of Complementary Regularizers,
IP(29), 2020, pp. 5310-5323.
IEEE DOI
2004
Transforms, Image denoising, Adaptation models, Image restoration,
Image reconstruction, Magnetic resonance imaging,
machine learning
BibRef
Xiang, Q.[Qian],
Peng, L.[Likun],
Pang, X.L.[Xue-Liang],
Image DAEs based on residual entropy maximum,
IET-IPR(14), No. 6, 11 May 2020, pp. 1164-1169.
DOI Link
2005
denoising auto-encoders. Learn mapping from noisy to target image.
BibRef
Jiao, J.B.[Jian-Bo],
Tu, W.C.[Wei-Chih],
Liu, D.[Ding],
He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W. H.],
Huang, T.S.[Thomas S.],
FormNet: Formatted Learning for Image Restoration,
IP(29), 2020, pp. 6302-6314.
IEEE DOI
2005
BibRef
Earlier: A1, A2, A4, A5, Only:
FormResNet: Formatted Residual Learning for Image Restoration,
NTIRE17(1034-1042)
IEEE DOI
1709
Image restoration, Task analysis, Noise reduction, Training,
Visualization, Noise measurement, Image reconstruction, CNN.
Image reconstruction, Image resolution, Neural networks.
BibRef
Xu, X.,
Li, M.,
Sun, W.,
Yang, M.,
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and
Video Denoising,
IP(29), 2020, pp. 7153-7165.
IEEE DOI
2007
Noise reduction, Noise measurement, Neural networks, Kernel,
Heuristic algorithms, Image denoising, Aggregates, Image denoising,
neural network
BibRef
Byun, J.[Jaeseok],
Moon, T.[Taesup],
Learning Blind Pixelwise Affine Image Denoiser With Single Noisy
Images,
SPLetters(27), 2020, pp. 1105-1109.
IEEE DOI
2007
Noise measurement, Noise reduction, Training, AWGN, Neural networks,
Computational modeling, Transforms,
variance stabilizing transformation
BibRef
Xia, H.Y.[Hai-Ying],
Zhu, F.Y.[Fu-Yu],
Li, H.S.[Hai-Sheng],
Song, S.X.[Shu-Xiang],
Mou, X.W.[Xiang-Wei],
Combination of multi-scale and residual learning in deep CNN for image
denoising,
IET-IPR(14), No. 10, August 2020, pp. 2013-2019.
DOI Link
2008
BibRef
Singh, G.[Gurprem],
Mittal, A.[Ajay],
Aggarwal, N.[Naveen],
ResDNN: deep residual learning for natural image denoising,
IET-IPR(14), No. 11, September 2020, pp. 2425-2434.
DOI Link
2009
BibRef
Guo, B.Y.[Bing-Yang],
Song, K.C.[Ke-Chen],
Dong, H.W.[Hong-Wen],
Yan, Y.H.[Yun-Hui],
Tu, Z.B.[Zhi-Biao],
Zhu, L.[Liu],
NERNet: Noise estimation and removal network for image denoising,
JVCIR(71), 2020, pp. 102851.
Elsevier DOI
2009
Image denoising, Convolutional neural networks,
Attention mechanism, Dilated convolution, Dilation rate selecting
BibRef
Li, X.X.[Xiao-Xia],
Xiao, J.[Juan],
Zhou, Y.Y.[Ying-Yue],
Ye, Y.Z.[Yuan-Zheng],
Lv, N.Z.[Nian-Zu],
Wang, X.Y.[Xue-Yuan],
Wang, S.N.[Shu-Nli],
Gao, S.B.[Shao-Bing],
Detail retaining convolutional neural network for image denoising,
JVCIR(71), 2020, pp. 102774.
Elsevier DOI
2009
Image denoising, Convolutional neural network,
Detail retaining, Image restoration, Gaussian denoising
BibRef
Tian, Y.J.[Ying-Jie],
Wang, Y.Q.[Yi-Qi],
Yang, L.R.[Lin-Rui],
Qi, Z.Q.[Zhi-Quan],
CANet: Concatenated Attention Neural Network for Image Restoration,
SPLetters(27), 2020, pp. 1615-1619.
IEEE DOI
2010
Task analysis, Image coding, Image restoration, Feature extraction,
Image denoising, Convolution, Neural networks,
attention mechanism
BibRef
Wu, T.,
Li, W.,
Jia, S.,
Dong, Y.,
Zeng, T.,
Deep Multi-Level Wavelet-CNN Denoiser Prior for Restoring Blurred
Image With Cauchy Noise,
SPLetters(27), 2020, pp. 1635-1639.
IEEE DOI
2010
Image restoration, Discrete wavelet transforms,
Computational modeling, Image denoising, Gaussian noise, mwcnn denoiser
BibRef
Wang, S.H.[Shu-Hui],
Hu, L.[Ling],
Li, L.[Liang],
Zhang, W.G.[Wei-Gang],
Huang, Q.M.[Qing-Ming],
Two-stream deep sparse network for accurate and efficient image
restoration,
CVIU(200), 2020, pp. 103029.
Elsevier DOI
2010
Two-stream sparse network, Image restoration,
Image super-resolution, Image denoising
BibRef
Fang, Y.Y.[Ying-Ying],
Zeng, T.Y.[Tie-Yong],
Learning deep edge prior for image denoising,
CVIU(200), 2020, pp. 103044.
Elsevier DOI
2010
Denoising, Variational model, Total variation, Edge prior, CNN, Interpretability
BibRef
Xu, J.[Jun],
Huang, Y.[Yuan],
Cheng, M.M.[Ming-Ming],
Liu, L.[Li],
Zhu, F.[Fan],
Xu, Z.[Zhou],
Shao, L.[Ling],
Noisy-as-Clean:
Learning Self-Supervised Denoising From Corrupted Image,
IP(29), 2020, pp. 9316-9329.
IEEE DOI
2010
Noise measurement, Noise reduction, Training, Image denoising, AWGN,
Benchmark testing, Electronics packaging, Image denoising,
convolutional neural network
BibRef
Chen, C.[Chang],
Xiong, Z.W.[Zhi-Wei],
Tian, X.M.[Xin-Mei],
Zha, Z.J.[Zheng-Jun],
Wu, F.[Feng],
Real-World Image Denoising with Deep Boosting,
PAMI(42), No. 12, December 2020, pp. 3071-3087.
IEEE DOI
2011
Neural networks, Noise reduction, Image denoising, Task analysis,
Image restoration, Transform coding, Computational modeling,
real-world noise
BibRef
Liu, C.[Chang],
Gao, Q.F.[Qi-Fan],
Wu, X.L.[Xiao-Lin],
Exaggerated Learning for Clean-and-Sharp Image Restoration,
ICIP20(673-677)
IEEE DOI
2011
Image restoration, Image edge detection, Image resolution,
Task analysis, Machine learning, Signal resolution, image restoration
BibRef
Zamir, S.W.[Syed Waqas],
Arora, A.[Aditya],
Khan, S.[Salman],
Hayat, M.[Munawar],
Khan, F.S.[Fahad Shahbaz],
Yang, M.H.[Ming-Hsuan],
Shao, L.[Ling],
Multi-Stage Progressive Image Restoration,
CVPR21(14816-14826)
IEEE DOI
2111
Runtime, Computational modeling, Noise reduction,
Performance gain, Image restoration
BibRef
Zamir, S.W.[Syed Waqas],
Arora, A.[Aditya],
Khan, S.[Salman],
Hayat, M.[Munawar],
Khan, F.S.[Fahad Shahbaz],
Yang, M.H.[Ming-Hsuan],
Shao, L.[Ling],
Learning Enriched Features for Fast Image Restoration and Enhancement,
PAMI(45), No. 2, February 2023, pp. 1934-1948.
IEEE DOI
2301
BibRef
Earlier:
Learning Enriched Features for Real Image Restoration and Enhancement,
ECCV20(XXV:492-511).
Springer DOI
2011
Feature extraction, Image restoration, Streaming media,
Spatial resolution, Image denoising, Cameras, Superresolution,
and contrast enhancement
BibRef
Ren, D.W.[Dong-Wei],
Zuo, W.M.[Wang-Meng],
Zhang, D.[David],
Zhang, L.[Lei],
Yang, M.H.[Ming-Hsuan],
Simultaneous Fidelity and Regularization Learning for Image
Restoration,
PAMI(43), No. 1, January 2021, pp. 284-299.
IEEE DOI
2012
Image restoration, Degradation, Rain, Deconvolution, Task analysis,
Kernel, Adaptation models, Image restoration, blind deconvolution,
task-driven learning
BibRef
Quan, Y.H.[Yu-Hui],
Chen, Y.X.[Yi-Xin],
Shao, Y.Z.[Yi-Zhen],
Teng, H.[Huan],
Xu, Y.[Yong],
Ji, H.[Hui],
Image denoising using complex-valued deep CNN,
PR(111), 2021, pp. 107639.
Elsevier DOI
2012
Complex-valued operations, Convolutional neural network,
Image denoising, Deep learning
BibRef
Chen, M.Q.[Ming-Qin],
Quan, Y.H.[Yu-Hui],
Pang, T.Y.[Tong-Yao],
Ji, H.[Hui],
Nonblind Image Deconvolution via Leveraging Model Uncertainty in An
Untrained Deep Neural Network,
IJCV(130), No. 7, July 2022, pp. 1770-1789.
Springer DOI
2207
BibRef
Chen, M.Q.[Ming-Qin],
Quan, Y.H.[Yu-Hui],
Xu, Y.[Yong],
Ji, H.[Hui],
Self-Supervised Blind Image Deconvolution via Deep Generative
Ensemble Learning,
CirSysVideo(33), No. 2, February 2023, pp. 634-647.
IEEE DOI
2302
Kernel, Generators, Electronics packaging, Deep learning, Training,
Estimation, Convolution, Blind image deconvolution,
dataset-free learning
BibRef
Quan, Y.H.[Yu-Hui],
Chen, Z.J.[Zhuo-Jie],
Zheng, H.[Huan],
Ji, H.[Hui],
Learning Deep Non-blind Image Deconvolution Without Ground Truths,
ECCV22(VI:642-659).
Springer DOI
2211
BibRef
Song, Y.,
Zhu, Y.,
Du, X.,
Grouped Multi-Scale Network for Real-World Image Denoising,
SPLetters(27), 2020, pp. 2124-2128.
IEEE DOI
2012
Convolution, Noise reduction, Feature extraction,
Noise measurement, Image denoising, Noise level,
noise modeling
BibRef
Wang, Y.,
Song, X.,
Chen, K.,
Channel and Space Attention Neural Network for Image Denoising,
SPLetters(28), 2021, pp. 424-428.
IEEE DOI
2103
Convolution, Noise level, Noise reduction, Image denoising,
Neural networks, Signal processing algorithms, Kernel, CNN,
PSNR
BibRef
Nguyen, H.V.,
Ulfarsson, M.O.,
Sveinsson, J.R.,
Hyperspectral Image Denoising Using SURE-Based Unsupervised
Convolutional Neural Networks,
GeoRS(59), No. 4, April 2021, pp. 3369-3382.
IEEE DOI
2104
Noise reduction, Noise measurement, Training,
Hyperspectral imaging, Image denoising,
unsupervised deep learning (DL)
BibRef
Pan, J.S.[Jin-Shan],
Dong, J.X.[Jiang-Xin],
Liu, Y.[Yang],
Zhang, J.W.[Jia-Wei],
Ren, J.[Jimmy],
Tang, J.H.[Jin-Hui],
Tai, Y.W.[Yu-Wing],
Yang, M.H.[Ming-Hsuan],
Physics-Based Generative Adversarial Models for Image Restoration and
Beyond,
PAMI(43), No. 7, July 2021, pp. 2449-2462.
IEEE DOI
2106
Image restoration, Generative adversarial networks,
Physics, Task analysis, Mathematical model,
image restoration
BibRef
Zhang, Y.L.[Yu-Lun],
Tian, Y.P.[Ya-Peng],
Kong, Y.[Yu],
Zhong, B.[Bineng],
Fu, Y.[Yun],
Residual Dense Network for Image Restoration,
PAMI(43), No. 7, July 2021, pp. 2480-2495.
IEEE DOI
2106
Feature extraction, Image restoration, Training, Task analysis,
Image coding, Image denoising, Residual dense network,
image deblurring
BibRef
Hou, R.Z.[Rui-Zhi],
Li, F.[Fang],
Error feedback denoising network,
IET-IPR(15), No. 7, 2021, pp. 1508-1517.
DOI Link
2106
deep convolutional neural networks, error feedback strategy, image denoising
BibRef
Wang, Y.M.[Yi-Ming],
Chang, D.X.[Dong-Xia],
Zhao, Y.[Yao],
A new blind image denoising method based on asymmetric generative
adversarial network,
IET-IPR(15), No. 6, 2021, pp. 1260-1272.
DOI Link
2106
BibRef
Han, L.[Lili],
Li, S.J.[Shu-Juan],
Liu, X.P.[Xiu-Ping],
Image denoising based on BCOLTA: Dataset and study,
IET-IPR(15), No. 3, 2021, pp. 624-633.
DOI Link
2106
block cosparsity overcomplete learning transform algorithm.
BibRef
Du, Y.[Yong],
Han, G.Q.[Guo-Qiang],
Tan, Y.J.[Yin-Jie],
Xiao, C.F.[Chu-Feng],
He, S.F.[Sheng-Feng],
Blind Image Denoising via Dynamic Dual Learning,
MultMed(23), 2021, pp. 2139-2152.
IEEE DOI
2107
Noise level, Image denoising, Noise reduction, Task analysis,
Noise measurement, Estimation, Optimization, Blind image denoising,
Gaussian noise
BibRef
Zhang, Y.[Yulun],
Li, K.[Kunpeng],
Li, K.[Kai],
Sun, G.[Gan],
Kong, Y.[Yu],
Fu, Y.[Yun],
Accurate and Fast Image Denoising via Attention Guided Scaling,
IP(30), 2021, pp. 6255-6265.
IEEE DOI
2107
Image denoising, Noise measurement, Training, Noise reduction,
Generative adversarial networks, Visualization, Task analysis,
semantic segmentation
BibRef
Ye, H.L.[Hai-Liang],
Li, H.[Hong],
Chen, C.L.P.[C. L. Philip],
Adaptive Deep Cascade Broad Learning System and Its Application in
Image Denoising,
Cyber(51), No. 9, September 2021, pp. 4450-4463.
IEEE DOI
2109
Image denoising, Zinc, Learning systems,
Stability criteria, Training, Broad learning system (BLS),
stability
BibRef
Lee, K.[Kanggeun],
Jeong, W.K.[Won-Ki],
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image
Denoising,
MedImg(40), No. 11, November 2021, pp. 3238-3248.
IEEE DOI
2111
Noise reduction, Noise measurement, Image denoising, Training,
Training data, Task analysis, Generators, Adversarial learning,
residual learning
BibRef
Ayyoubzadeh, S.M.[Seyed Mehdi],
Wu, X.L.[Xiao-Lin],
High Frequency Detail Accentuation in CNN Image Restoration,
IP(30), 2021, pp. 8836-8846.
IEEE DOI
2111
Image restoration, Task analysis, Training,
Convolutional neural networks, Image edge detection, denoising
BibRef
Khowaja, S.A.[Sunder Ali],
Yahya, B.N.[Bernardo Nugroho],
Lee, S.L.[Seok-Lyong],
Cascaded and Recursive ConvNets (CRCNN):
An effective and flexible approach for image denoising,
SP:IC(99), 2021, pp. 116420.
Elsevier DOI
2111
Image denoising, Spatial variant noise,
Hybrid orthogonal regularization, Convolutional neural networks
BibRef
Zhai, S.[Sen],
Ren, C.[Chao],
Wang, Z.Y.[Zheng-Yong],
He, X.H.[Xiao-Hai],
Qing, L.B.[Lin-Bo],
An effective deep network using target vector update modules for
image restoration,
PR(122), 2022, pp. 108333.
Elsevier DOI
2112
Image restoration, Plug and play method,
Convolutional neural network framework, Target vector update module
BibRef
Kong, S.J.[Sheng-Jiang],
Wang, W.W.[Wei-Wei],
Feng, X.C.[Xiang-Chu],
Jia, X.X.[Xi-Xi],
Deep RED Unfolding Network for Image Restoration,
IP(31), 2022, pp. 852-867.
IEEE DOI
2201
Image restoration, Noise reduction, Data models, Task analysis,
Closed-form solutions, Noise level, Mathematical models,
image restoration
BibRef
Zhang, H.K.[Hao-Kui],
Li, Y.[Ying],
Chen, H.[Hao],
Gong, C.R.[Cheng-Rong],
Bai, Z.W.[Zong-Wen],
Shen, C.H.[Chun-Hua],
Memory-Efficient Hierarchical Neural Architecture Search for Image
Restoration,
IJCV(130), No. 1, January 2022, pp. 157-178.
Springer DOI
2201
BibRef
Earlier: A1, A2, A3, A6, Ohly:
Memory-Efficient Hierarchical Neural Architecture Search for Image
Denoising,
CVPR20(3654-3663)
IEEE DOI
2008
Task analysis, Convolution, Image denoising,
Search problems, Computational modeling, Computer vision
BibRef
Li, Y.P.[Yu-Ping],
Nie, X.L.[Xiang-Li],
Diao, W.H.[Wen-Hui],
Zheng, S.[Suiwu],
Lifelong CycleGAN for continual multi-task image restoration,
PRL(153), 2022, pp. 183-189.
Elsevier DOI
2201
Image restoration, Lifelong/continual learning, CycleGAN, Knowledge distillation
BibRef
Yao, C.[Cheng],
Tang, Y.[Yibin],
Sun, J.[Jia],
Gao, Y.[Yuan],
Zhu, C.P.[Chang-Ping],
Multiscale residual fusion network for image denoising,
IET-IPR(16), No. 3, 2022, pp. 878-887.
DOI Link
2202
BibRef
Han, L.T.[Lin-Tao],
Zhao, Y.C.[Yu-Chen],
Lv, H.Y.[Heng-Yi],
Zhang, Y.[Yisa],
Liu, H.L.[Hai-Long],
Bi, G.L.[Guo-Ling],
Remote Sensing Image Denoising Based on Deep and Shallow Feature
Fusion and Attention Mechanism,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link
2203
BibRef
Al-Shabili, A.H.[Abdullah H.],
Selesnick, I.[Ivan],
Positive Sparse Signal Denoising: What Does a CNN Learn?,
SPLetters(29), 2022, pp. 912-916.
IEEE DOI
2205
Noise reduction, Convolutional neural networks,
Smoothing methods, Neural networks, Task analysis, Training,
proximal operator
BibRef
Gao, S.Q.[Shang-Qi],
Zhuang, X.H.[Xia-Hai],
Rank-One Network: An Effective Framework for Image Restoration,
PAMI(44), No. 6, June 2022, pp. 3224-3238.
IEEE DOI
2205
Image restoration, Image reconstruction, Image denoising,
Neural networks, Task analysis, Matrix decomposition,
neural network
BibRef
Luo, Z.D.[Zheng-Ding],
Shi, D.Y.[Dong-Yuan],
Gan, W.S.[Woon-Seng],
A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on
Deep Learning,
SPLetters(29), 2022, pp. 1102-1106.
IEEE DOI
2205
Filtering algorithms, Signal processing algorithms,
Band-pass filters, Noise reduction, Classification algorithms, hearables
BibRef
Jiu, M.Y.[Ming-Yuan],
Pustelnik, N.[Nelly],
Alternative Design of DeepPDNet in the Context of Image Restoration,
SPLetters(29), 2022, pp. 932-936.
IEEE DOI
2205
Image restoration, Neural networks, Signal processing algorithms,
Degradation, Backpropagation, Training,
sparsity
BibRef
Ma, R.J.[Rui-Jun],
Li, S.Y.[Shu-Yi],
Zhang, B.[Bob],
Li, Z.M.[Zheng-Ming],
Towards Fast and Robust Real Image Denoising With Attentive Neural
Network and PID Controller,
MultMed(24), 2022, pp. 2366-2377.
IEEE DOI
2205
Noise reduction, Noise measurement, Image denoising,
Feature extraction, Adaptation models, Process control,
PID Controller
BibRef
Liu, J.[Jing],
Liu, R.C.[Run-Chuan],
Zhao, S.S.[Shan-Shan],
Blind denoising using dense hybrid convolutional network,
IET-IPR(16), No. 8, 2022, pp. 2133-2147.
DOI Link
2205
BibRef
Li, Z.X.[Zhuo-Xiao],
Wang, F.Q.[Fa-Qiang],
Cui, L.[Li],
Liu, J.[Jun],
Dual Mixture Model Based CNN for Image Denoising,
IP(31), 2022, pp. 3618-3629.
IEEE DOI
2206
Image denoising, Mixture models, Feature extraction,
Convolutional neural networks, Convolution, Task analysis,
Non-Gaussian
BibRef
Meng, J.Y.[Jun-Ying],
Wang, F.Q.[Fa-Qiang],
Liu, J.[Jun],
Learnable Nonlocal Self-Similarity of Deep Features for Image
Denoising,
SIIMS(17), No. 1, 2024, pp. 441-475.
DOI Link
2404
BibRef
Xu, J.Y.[Jing-Yi],
Deng, X.[Xin],
Xu, M.[Mai],
Revisiting Convolutional Sparse Coding for Image Denoising:
From a Multi-Scale Perspective,
SPLetters(29), 2022, pp. 1202-1206.
IEEE DOI
2206
Image denoising, Dictionaries, Convolutional codes, Task analysis,
Convolution, Image reconstruction, Mathematical models, image denoising
BibRef
Zhang, Y.[Yu],
Zhang, D.[Dan],
Han, Z.[Zhen],
Jiang, P.[Peng],
A Joint Denoising Learning Model for Weight Update Space-Time
Diversity Method,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Zhao, M.[Mo],
Cao, G.[Gang],
Huang, X.L.[Xiang-Lin],
Yang, L.F.[Li-Fang],
Hybrid Transformer-CNN for Real Image Denoising,
SPLetters(29), 2022, pp. 1252-1256.
IEEE DOI
2206
Transformers, Noise reduction, Decoding, Convolution,
Computational modeling, Computational efficiency, Visualization,
RBF attention
BibRef
Ma, J.Y.[Jia-Yi],
Peng, C.[Chengli],
Tian, X.[Xin],
Jiang, J.J.[Jun-Jun],
DBDnet: A Deep Boosting Strategy for Image Denoising,
MultMed(24), 2022, pp. 3157-3168.
IEEE DOI
2206
Boosting, Noise reduction, Image denoising, Task analysis,
Deep learning, Learning systems, Noise measurement, image restoration
BibRef
Huang, T.[Tao],
Li, S.[Songjiang],
Jia, X.[Xu],
Lu, H.C.[Hu-Chuan],
Liu, J.Z.[Jian-Zhuang],
Neighbor2Neighbor: A Self-Supervised Framework for Deep Image
Denoising,
IP(31), 2022, pp. 4023-4038.
IEEE DOI
2206
Noise measurement, Noise reduction, Training, Image denoising,
Task analysis, Convolutional neural networks, Optimization,
deep neural networks
BibRef
Chen, Z.Y.[Ze-Yuan],
Jiang, Y.F.[Yi-Fan],
Liu, D.[Dong],
Wang, Z.Y.[Zhang-Yang],
CERL: A Unified Optimization Framework for Light Enhancement With
Realistic Noise,
IP(31), 2022, pp. 4162-4172.
IEEE DOI
2206
Noise reduction, Optimization, Task analysis, Noise measurement,
Adaptation models, Training, Lighting, Image denoising,
unsupervised learning
BibRef
Herbreteau, S.[Sébastien],
Kervrann, C.[Charles],
DCT2net: An Interpretable Shallow CNN for Image Denoising,
IP(31), 2022, pp. 4292-4305.
IEEE DOI
2207
Discrete cosine transforms, Noise reduction,
Convolutional neural networks, Transforms, Kernel, Convolution,
artifact removal
BibRef
Bahnemiri, S.G.[Sheyda Ghanbaralizadeh],
Ponomarenko, M.[Mykola],
Egiazarian, K.[Karen],
Learning-Based Noise Component Map Estimation for Image Denoising,
SPLetters(29), 2022, pp. 1407-1411.
IEEE DOI
2207
Estimation, Training, Noise measurement, Noise reduction,
Image denoising, Image color analysis,
deep convolutional neural networks
BibRef
Jiang, B.[Bo],
Lu, Y.[Yao],
Wang, J.H.[Jia-Huan],
Lu, G.M.[Guang-Ming],
Zhang, D.[David],
Deep Image Denoising With Adaptive Priors,
CirSysVideo(32), No. 8, August 2022, pp. 5124-5136.
IEEE DOI
2208
Noise reduction, Image reconstruction, Decoding, Image restoration,
Adaptive systems, Training, Noise measurement, Image denoising,
supplemental priors
BibRef
Jiang, B.[Bo],
Lu, Y.[Yao],
Chen, X.S.[Xiao-Sheng],
Lu, X.H.[Xin-Hai],
Lu, G.M.[Guang-Ming],
Graph Attention in Attention Network for Image Denoising,
SMCS(53), No. 11, November 2023, pp. 7077-7088.
IEEE DOI
2310
BibRef
Yu, K.[Ke],
Wang, X.T.[Xin-Tao],
Dong, C.[Chao],
Tang, X.[Xiaoou],
Loy, C.C.[Chen Change],
Path-Restore: Learning Network Path Selection for Image Restoration,
PAMI(44), No. 10, October 2022, pp. 7078-7092.
IEEE DOI
2209
Image restoration, Task analysis, Distortion, Noise reduction,
Complexity theory, Reinforcement learning, Training,
deep reinforcement learning
BibRef
Liu, H.S.[Hao-Sen],
Li, L.[Laquan],
Lu, J.B.[Jiang-Bo],
Tan, S.[Shan],
Group Sparsity Mixture Model and Its Application on Image Denoising,
IP(31), 2022, pp. 5677-5690.
IEEE DOI
2209
Noise reduction, Image denoising, Task analysis, Mixture models,
Analytical models, Learning systems, Sparse matrices,
nonlocal similarity
BibRef
Liu, H.S.[Hao-Sen],
Liu, X.[Xuan],
Lu, J.B.[Jiang-Bo],
Tan, S.[Shan],
Self-Supervised Image Prior Learning with GMM from a Single Noisy
Image,
ICCV21(2825-2834)
IEEE DOI
2203
Deep learning, Training, Estimation,
Eigenvalues and eigenfunctions, Image restoration,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yuan, W.[Wei],
Liu, H.[Han],
Liang, L.[Lili],
Image restoration via exponential scale mixture-based simultaneous
sparse prior,
IET-IPR(16), No. 12, 2022, pp. 3268-3283.
DOI Link
2209
BibRef
Fu, B.[Bo],
Wang, L.Y.[Li-Yan],
Luo, Z.X.[Zhong-Xuan],
A Robust Image Denoising Method With Multiview Texture-Aware
Convolutional Neural Networks,
MultMedMag(29), No. 3, July 2022, pp. 80-90.
IEEE DOI
2209
Noise reduction, Image denoising, Task analysis, Image restoration,
Neural networks, Noise measurement, Convolutional neural networks
BibRef
Yin, H.T.[Hai-Tao],
Wang, T.Y.[Tian-You],
Deep side group sparse coding network for image denoising,
IET-IPR(17), No. 1, 2023, pp. 1-11.
DOI Link
2301
BibRef
Wang, W.[Wei],
Wen, F.[Fei],
Yan, Z.[Zeyu],
Liu, P.L.[Pei-Lin],
Optimal Transport for Unsupervised Denoising Learning,
PAMI(45), No. 2, February 2023, pp. 2104-2118.
IEEE DOI
2301
Image restoration, Noise measurement, Noise reduction, Degradation,
Training, Training data, Image reconstruction, Restoration,
depth image denoising
BibRef
Ning, Q.[Qian],
Dong, W.S.[Wei-Sheng],
Li, X.[Xin],
Wu, J.J.[Jin-Jian],
Searching Efficient Model-Guided Deep Network for Image Denoising,
IP(32), 2023, pp. 668-681.
IEEE DOI
2301
Search problems, Image denoising, Noise reduction,
Task analysis, Image restoration, efficient deep neural network
BibRef
Dewil, V.[Valéry],
Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images,
IPOL(12), 2022, pp. 652-661.
DOI Link
2301
Code, Raw Data.
See also Unprocessing Images for Learned Raw Denoising.
BibRef
li, Q.F.[Qi-Fan],
Denoising image by matrix factorization in U-shaped convolutional
neural network,
JVCIR(90), 2023, pp. 103729.
Elsevier DOI
2301
Image denoising, Matrix factorization, Convolution network, Image restoration
BibRef
Liu, Y.K.[Yu-Kun],
Wan, B.[Bowen],
Shi, D.M.[Da-Ming],
Cheng, X.C.[Xiao-Chun],
Generative Recorrupted-to-Recorrupted: An Unsupervised Image
Denoising Network for Arbitrary Noise Distribution,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Huang, Z.H.[Zheng-Hua],
Zhu, Z.F.[Zi-Fan],
Wang, Z.C.[Zhi-Cheng],
Li, X.[Xi],
Xu, B.[Biyun],
Zhang, Y.Z.[Yao-Zong],
Fang, H.[Hao],
D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed
Noise Removal in Remotely Sensed Images,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Liu, J.[Jin],
Yang, Y.[Yang],
Xu, B.[Biyun],
Yu, H.[Hao],
Zhang, Y.Z.[Yao-Zong],
Li, Q.[Qian],
Huang, Z.H.[Zheng-Hua],
RSTC: Residual Swin Transformer Cascade to approximate Taylor
expansion for image denoising,
CVIU(248), 2024, pp. 104132.
Elsevier DOI
2409
Taylor expansion approximation, Residual swin transformer cascade (RSTC),
Image denoising
BibRef
Huang, Z.H.[Zheng-Hua],
Zhu, Z.[Zifan],
Zhang, Y.Z.[Yao-Zong],
Wang, Z.C.[Zhi-Cheng],
Xu, B.[Biyun],
Liu, J.[Jun],
Li, S.Y.[Shao-Yi],
Fang, H.[Hao],
MD3: Model-Driven Deep Remotely Sensed Image Denoising,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Jiang, Z.[Zutao],
Li, C.L.[Chang-Lin],
Chang, X.J.[Xiao-Jun],
Chen, L.[Ling],
Zhu, J.[Jihua],
Yang, Y.[Yi],
Dynamic Slimmable Denoising Network,
IP(32), 2023, pp. 1583-1598.
IEEE DOI
2303
Noise reduction, Image denoising, Logic gates, Neural networks,
Noise measurement, Convolutional neural networks, Deep learning,
dynamic inference
BibRef
Zheng, D.[Dihan],
Zhang, X.W.[Xiao-Wen],
Ma, K.[Kaisheng],
Bao, C.L.[Cheng-Long],
Learn From Unpaired Data for Image Restoration:
A Variational Bayes Approach,
PAMI(45), No. 5, May 2023, pp. 5889-5903.
IEEE DOI
2304
Image restoration, Degradation, Data models, Task analysis,
Noise measurement, Training data, Superresolution,
variational auto-encoder
BibRef
Liu, S.C.[Shuai-Cheng],
Lu, Y.H.[Yu-Hang],
Jiang, H.[Hai],
Ye, N.J.[Nian-Jin],
Wang, C.[Chuan],
Zeng, B.[Bing],
Unsupervised Global and Local Homography Estimation With Motion Basis
Learning,
PAMI(45), No. 6, June 2023, pp. 7885-7899.
IEEE DOI
2305
Feature extraction, Estimation, Transforms, Optimization,
Optical imaging, Optical distortion, Matrix converters,
subspace projection
BibRef
Zheng, Y.H.[Yi-Hao],
Luo, K.M.[Kun-Ming],
Liu, S.C.[Shuai-Cheng],
Li, Z.[Zun],
Xiang, Y.[Ye],
Wu, L.F.[Li-Fang],
Zeng, B.[Bing],
Chen, C.W.[Chang Wen],
GLOCAL: A self-supervised learning framework for global and local
motion estimation,
PRL(178), 2024, pp. 91-97.
Elsevier DOI
2402
Video understanding, motion pattern, optical flow, motion estimation
BibRef
Ye, N.J.[Nian-Jin],
Wang, C.[Chuan],
Fan, H.Q.[Hao-Qiang],
Liu, S.C.[Shuai-Cheng],
Motion Basis Learning for Unsupervised Deep Homography Estimation
with Subspace Projection,
ICCV21(13097-13105)
IEEE DOI
2203
Codes, Estimation, Benchmark testing, Feature extraction,
Optimization, Motion and tracking,
BibRef
Cheng, S.[Shen],
Wang, Y.Z.[Yu-Zhi],
Huang, H.B.[Hai-Bin],
Liu, D.H.[Dong-Hao],
Fan, H.Q.[Hao-Qiang],
Liu, S.C.[Shuai-Cheng],
NBNet: Noise Basis Learning for Image Denoising with Subspace
Projection,
CVPR21(4894-4904)
IEEE DOI
2111
Convolution, Computational modeling,
Noise reduction, Network architecture,
Computational efficiency
BibRef
Zhang, D.X.[Dong-Xu],
Yan, Y.[Yang],
Huang, Y.L.[Yu-Lin],
Liu, B.[Bowen],
Zheng, Q.B.[Qing-Bing],
Zhang, J.[Jun],
Xia, N.S.[Ning-Shao],
Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep
Convolutional Autoencoder and K-Means++,
MedImg(42), No. 5, May 2023, pp. 1509-1521.
IEEE DOI
2305
Training, Reliability, Noise reduction, Image reconstruction,
Signal to noise ratio, Clustering methods, Neural networks,
variational autoencoder
BibRef
Thakur, R.K.[Ramesh Kumar],
Maji, S.K.[Suman Kumar],
Multi scale pixel attention and feature extraction based neural
network for image denoising,
PR(141), 2023, pp. 109603.
Elsevier DOI
2306
Blind Gaussian noise removal, Deep convolutional residual network,
Convolutional layer, Skip connection
BibRef
Liu, C.[Chang],
Hu, X.L.[Xian-Liang],
Deep neural network with deformable convolution and side window
convolution for image denoising,
PRL(171), 2023, pp. 92-98.
Elsevier DOI
2306
Image denoising, Irregular convolution, Neural networks,
Block structure, Side window convolution
BibRef
Achddou, R.[Raphaël],
Gousseau, Y.[Yann],
Ladjal, S.[Saïd],
Fully synthetic training for image restoration tasks,
CVIU(233), 2023, pp. 103723.
Elsevier DOI
2307
BibRef
Earlier:
Synthetic Images as a Regularity Prior for Image Restoration Neural
Networks,
SSVM21(333-345).
Springer DOI
2106
Image restoration, image denoising, Statistical modeling
BibRef
Elad, M.[Michael],
Kawar, B.[Bahjat],
Vaksman, G.[Gregory],
Image Denoising: The Deep Learning Revolution and Beyond: A Survey
Paper,
SIIMS(16), No. 3, 2023, pp. 1594-1654.
DOI Link
2309
Survey, Denoising.
BibRef
Wang, J.H.[Jia-Huan],
Lu, Y.[Yao],
Lu, G.M.[Guang-Ming],
Lightweight image denoising network with four-channel interaction
transform,
IVC(137), 2023, pp. 104766.
Elsevier DOI
2309
Image denoising, Lightweight network, Four-channel interaction transform
BibRef
Jiang, B.[Bo],
Lu, Y.[Yao],
Zhang, B.[Bob],
Lu, G.M.[Guang-Ming],
Few-Shot Learning for Image Denoising,
CirSysVideo(33), No. 9, September 2023, pp. 4741-4753.
IEEE DOI
2310
BibRef
Zhou, Z.[Zheng],
Chen, Y.Y.[Yong-Yong],
Zhou, Y.C.[Yi-Cong],
Deep Dynamic Memory Augmented Attentional Dictionary Learning for
Image Denoising,
CirSysVideo(33), No. 9, September 2023, pp. 4784-4797.
IEEE DOI
2310
BibRef
Chen, S.Q.[Shi-Qi],
Zhou, J.W.[Jing-Wen],
Li, M.[Menghao],
Chen, Y.T.[Yue-Ting],
Jiang, T.T.[Ting-Ting],
Mobile image restoration via prior quantization,
PRL(174), 2023, pp. 64-70.
Elsevier DOI
2310
Image restoration, Mobile ISP systems,
Computational photography, Deep learning
BibRef
Zhang, J.[Jing],
Sang, L.[Liu],
Zhang, Z.C.[Zhi-Cheng],
Shao, M.[Minhao],
Li, Y.S.[Yun-Song],
V-shaped neural network structure based on multi-scale features for
image denoising,
JVCIR(97), 2023, pp. 103952.
Elsevier DOI
2312
Image denoising, Fixed-scale, Multi-scale, Sampling, V-shaped subnetwork
BibRef
Zhang, J.[Jing],
Sang, L.[Liu],
Wan, Z.K.[Ze-Kang],
Wang, Y.C.[Yu-Chen],
Li, Y.S.[Yun-Song],
Deep Convolutional Neural Network Based on Multi-Scale Feature
Extraction for Image Denoising,
VCIP20(213-216)
IEEE DOI
2102
Feature extraction, Noise reduction, Image denoising, Noise level,
Image reconstruction, Diamond, Training, image denoising,
diamond
BibRef
Zhang, K.[Ke],
Long, M.[Miao],
Chen, J.[Jie],
Liu, M.Z.[Ming-Zhu],
Li, J.J.[Jing-Jing],
CFPNet: A Denoising Network for Complex Frequency Band Signal
Processing,
MultMed(25), 2023, pp. 8212-8224.
IEEE DOI
2312
BibRef
Cai, Z.Y.[Zhao-Yuan],
Xie, X.H.[Xiang-Hua],
Deng, J.J.[Jing-Jing],
Dou, Z.F.[Zeng-Fa],
Tong, B.[Bo],
Ma, X.[Xiaoke],
Image restoration with group sparse representation and low-rank group
residual learning,
IET-IPR(18), No. 3, 2024, pp. 741-760.
DOI Link
2402
group residual learning, group sparse representation,
image restoration, low-rank self-representation
BibRef
Jin, L.J.[Lu-Jia],
Guo, Q.[Qing],
Zhao, S.[Shi],
Zhu, L.[Lei],
Chen, Q.[Qian],
Ren, Q.S.[Qiu-Shi],
Lu, Y.Y.[Yan-Ye],
One-Pot Multi-frame Denoising,
IJCV(132), No. 2, February 2024, pp. 515-536.
Springer DOI
2402
Code:
WWW Link. Learning based denoising
BibRef
Wu, W.C.[Wen-Cong],
Liu, S.J.[Shi-Jie],
Xia, Y.L.[Yue-Long],
Zhang, Y.G.[Yun-Gang],
Dual Residual Attention Network for Image Denoising,
PR(149), 2024, pp. 110291.
Elsevier DOI Code:
WWW Link.
2403
Image denoising, Dual deep convolutional network,
Residual attention learning, Hybrid residual attention learning
BibRef
Du, J.Z.[Jia-Zhi],
Qiao, X.[Xin],
Yan, Z.[Zifei],
Zhang, H.Z.[Hong-Zhi],
Zuo, W.M.[Wang-Meng],
Flexible image denoising model with multi-layer conditional feature
modulation,
PR(152), 2024, pp. 110372.
Elsevier DOI
2405
Image denoising, Convolutional neural network,
Additive white Gaussian noise, Feature modulation
BibRef
Zafar, A.[Anas],
Aftab, D.[Danyal],
Qureshi, R.[Rizwan],
Fan, X.Q.[Xin-Qi],
Chen, P.J.[Ping-Jun],
Wu, J.[Jia],
Ali, H.[Hazrat],
Nawaz, S.[Shah],
Khan, S.[Sheheryar],
Shah, M.[Mubarak],
Single Stage Adaptive Multi-Attention Network for Image Restoration,
IP(33), 2024, pp. 2924-2935.
IEEE DOI
2405
Image restoration, Task analysis, Computer architecture,
Convolution, Computational modeling, Computational efficiency, deep learning
BibRef
Fan, L.W.[Lin-Wei],
Yan, X.Y.[Xiao-Yu],
Li, H.Y.[Hui-Yu],
Zhang, Y.X.[Yong-Xia],
Liu, H.[Hui],
Zhang, C.M.[Cai-Ming],
Bidirectional image denoising with blurred image feature,
PR(153), 2024, pp. 110563.
Elsevier DOI
2405
Image denoising, CNN, Deep learning, Blurred image feature,
bidirectional denoising process
BibRef
Fan, L.W.[Lin-Wei],
Cui, J.[Jin],
Li, H.Y.[Hui-Yu],
Yan, X.Y.[Xiao-Yu],
Liu, H.[Hui],
Zhang, C.M.[Cai-Ming],
Complementary Blind-Spot Network for Self-Supervised Real Image
Denoising,
CirSysVideo(34), No. 10, October 2024, pp. 10107-10120.
IEEE DOI Code:
WWW Link.
2411
Noise, Noise reduction, Noise measurement, Image denoising, Training,
Image restoration, Correlation, Self-supervised denoising,
blind-spot network
BibRef
Zhao, S.B.[Shao-Bo],
Dong, Y.Q.[You-Qiang],
Cheng, X.[Xi],
Huo, Y.[Yu],
Zhang, M.[Min],
Wang, H.[Hai],
Remote Sensing Image Denoising Based on Feature Interaction
Complementary Learning,
RS(16), No. 20, 2024, pp. 3820.
DOI Link
2411
BibRef
Zhang, C.X.[Chen-Xiao],
Deng, X.[Xin],
Sun, H.P.[Hong-Peng],
Xu, J.Y.[Jing-Yi],
Xu, M.[Mai],
SN-NET: Semismooth Newton Driven Lightweight Network for Real-World
Image Denoising,
ICIP24(1424-1430)
IEEE DOI Code:
WWW Link.
2411
Knowledge engineering, Optimization methods, Network architecture,
Convex functions, Image restoration, Semismooth Newton
BibRef
Fadnavis, S.[Shreyas],
Chowdhury, A.[Agniva],
Batson, J.[Joshua],
Drineas, P.[Petros],
Garyfallidis, E.[Eleftherios],
Patch2Self2: Self-Supervised Denoising on Coresets via Matrix
Sketching,
CVPR24(27631-27641)
IEEE DOI
2410
Training, Magnetic resonance imaging, Noise reduction,
Memory management, Focusing, Estimation, Probability,
self-supervised learning
BibRef
Khudjaev, N.[Nodirkhuja],
Tsoy, R.[Roman],
Sharif, S.M.A.,
Myrzabekov, A.[Azamat],
Kim, S.[Seongwan],
Lee, J.[Jaeho],
Dformer: Learning Efficient Image Restoration with Perceptual
Guidance,
NTIRE24(6363-6372)
IEEE DOI
2410
Image coding, Image edge detection, Lighting, Deep architecture,
Image restoration, transformer
BibRef
Yang, K.Z.[Kang-Zhen],
Hu, T.[Tao],
Dai, K.[Kexin],
Chen, G.G.[Geng-Geng],
Cao, Y.[Yu],
Dong, W.[Wei],
Wu, P.[Peng],
Zhang, Y.N.[Yan-Ning],
Yan, Q.S.[Qing-Sen],
CRNet: A Detail-Preserving Network for Unified Image Restoration and
Enhancement Task,
NTIRE24(6086-6096)
IEEE DOI
2410
Target tracking, Computational modeling, Noise reduction, Noise,
Imaging, Visual effects, Image restoration, Low-level
BibRef
Ouyang, X.[Xu],
Chen, Y.[Ying],
Zhu, K.Y.[Kai-Yue],
Agam, G.[Gady],
Image restoration refinement with Uformer GAN,
NTIRE24(5919-5928)
IEEE DOI
2410
Training, Convolution, Image synthesis, Neural networks,
Generative adversarial networks, Transformers, Image restoration, refinement
BibRef
Chihaoui, H.[Hamadi],
Favaro, P.[Paolo],
Masked and Shuffled Blind Spot Denoising for Real-World Images,
CVPR24(3025-3034)
IEEE DOI Code:
WWW Link.
2410
Multi-stage noise shaping, Training, Correlation,
Image color analysis, Noise reduction, Noise, self-supervised image denoising
BibRef
Chen, S.Y.[Shi-Yan],
Zhang, J.Y.[Ji-Yuan],
Yu, Z.F.[Zhao-Fei],
Huang, T.J.[Tie-Jun],
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised
Denoising in Real-World Scenarios,
CVPR24(2814-2823)
IEEE DOI
2410
Training, Correlation, Noise reduction, Noise, Visual effects,
Self-Supervised Denoising, Blind-Spot Network
BibRef
Xu, X.G.[Xiao-Gang],
Kong, S.[Shu],
Hu, T.[Tao],
Liu, Z.[Zhe],
Bao, H.J.[Hu-Jun],
Boosting Image Restoration via Priors from Pre-Trained Models,
CVPR24(2900-2909)
IEEE DOI
2410
Shape, Computational modeling, Noise reduction, Training data,
Boosting, Data models, Pre-trained models, Image restoration,
Channel-Spatial Attention
BibRef
Li, R.[Ruoqi],
Liu, C.[Chang],
Wang, Z.[Ziyi],
Du, Y.[Yao],
Yang, J.J.[Jing-Jing],
Bao, L.[Long],
Sun, H.[Heng],
From Synthetic to Real:
A Calibration-free Pipeline for Few-shot Raw Image Denoising,
MIPI24(1106-1114)
IEEE DOI
2410
Target recognition, Noise, Pipelines, Training data,
Sensor phenomena and characterization, Feature extraction,
Calibration-free noise synthetic
BibRef
Yasarla, R.[Rajeev],
Valanarasu, J.M.J.[Jeya Maria Jose],
Vishwanath, S.,
Patel, V.M.[Vishal M.],
Self-Supervised Denoising Transformer with Gaussian Process,
WACV24(1463-1473)
IEEE DOI
2404
Noise reduction, Noise, Gaussian processes,
Self-supervised learning, Network architecture,
Low-level and physics-based vision
BibRef
Deng, X.[Xin],
Gao, C.[Chao],
Xu, M.[Mai],
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet
Lifting,
ICCV23(22311-22320)
IEEE DOI
2401
BibRef
Liu, Y.L.[Yi-Lin],
Li, J.[Jiang],
Pang, Y.[Yunkui],
Nie, D.[Dong],
Yap, P.T.[Pew-Thian],
The Devil is in the Upsampling:
Architectural Decisions Made Simpler for Denoising with Deep Image Prior,
ICCV23(12374-12383)
IEEE DOI
2401
BibRef
Wang, J.C.[Jia-Chuan],
Di, S.[Shimin],
Chen, L.[Lei],
Ng, C.W.W.[Charles Wang Wai],
Noise2Info: Noisy Image to Information of Noise for Self-Supervised
Image Denoising,
ICCV23(15988-15997)
IEEE DOI
2401
BibRef
Achddou, R.[Raphaël],
Gousseau, Y.[Yann],
Ladjal, S.[SaÏd],
Learning Raw Image Denoising Using a Parametric Color Image Model,
ICIP23(2690-2694)
IEEE DOI
2312
BibRef
Liu, Z.Q.[Zhuo-Qun],
Jin, M.[Meiguang],
Chen, Y.[Ying],
Liu, H.[Huaida],
Yang, C.[Canqian],
Xiong, H.K.[Hong-Kai],
Lightweight Network Towards Real-Time Image Denoising On Mobile
Devices,
ICIP23(2270-2274)
IEEE DOI
2312
BibRef
Ye, J.[Juncheol],
Yeo, H.[Hyunho],
Park, J.[Jinwoo],
Han, D.[Dongsu],
AccelIR: Task-aware Image Compression for Accelerating Neural
Restoration,
CVPR23(18216-18226)
IEEE DOI
2309
BibRef
Li, T.H.[Tai-Hui],
Wang, H.K.[Heng-Kang],
Zhuang, Z.[Zhong],
Sun, J.[Ju],
Deep Random Projector: Accelerated Deep Image Prior,
CVPR23(18176-18185)
IEEE DOI
2309
BibRef
Zhang, D.[Dan],
Zhou, F.F.[Fang-Fang],
Jiang, Y.[Yuwen],
Fu, Z.M.[Zheng-Ming],
MM-BSN: Self-Supervised Image Denoising for Real-World with
Multi-Mask based on Blind-Spot Network,
UG23(4189-4198)
IEEE DOI
2309
BibRef
Zhao, H.[Haiyu],
Gou, Y.B.[Yuan-Biao],
Li, B.[Boyun],
Peng, D.Z.[De-Zhong],
Lv, J.C.[Jian-Cheng],
Peng, X.[Xi],
Comprehensive and Delicate:
An Efficient Transformer for Image Restoration,
CVPR23(14122-14132)
IEEE DOI
2309
BibRef
Fahim, M.A.N.I.[Masud An-Nur Islam],
Boutellier, J.[Jani],
SS-TTA: Test-Time Adaption for Self-Supervised Denoising Methods,
NTIRE23(1178-1187)
IEEE DOI
2309
BibRef
Zhang, J.H.[Jing-Hao],
Huang, J.[Jie],
Yao, M.D.[Ming-De],
Yang, Z.Z.[Zi-Zheng],
Yu, H.[Hu],
Zhou, M.[Man],
Zhao, F.[Feng],
Ingredient-oriented Multi-Degradation Learning for Image Restoration,
CVPR23(5825-5835)
IEEE DOI
2309
BibRef
Vaksman, G.[Gregory],
Elad, M.[Michael],
PatchCraft Self-Supervised Training for Correlated Image Denoising,
CVPR23(5795-5804)
IEEE DOI
2309
BibRef
Chen, H.Y.[Hao-Yu],
Gu, J.J.[Jin-Jin],
Liu, Y.H.[Yi-Hao],
Magid, S.A.[Salma Abdel],
Dong, C.[Chao],
Wang, Q.[Qiong],
Pfister, H.[Hanspeter],
Zhu, L.[Lei],
Masked Image Training for Generalizable Deep Image Denoising,
CVPR23(1692-1703)
IEEE DOI
2309
BibRef
Lee, S.[Seunghwan],
Kim, T.H.[Tae Hyun],
Noisetransfer: Image Noise Generation with Contrastive Embeddings,
ACCV22(III:323-339).
Springer DOI
2307
BibRef
Zhang, Y.[Yali],
Wang, X.F.[Xiao-Fan],
Wang, F.[Fengpin],
Wang, J.J.[Jin-Jia],
Image Denoising Using Convolutional Sparse Coding Network with Dry
Friction,
ACCV22(I:587-601).
Springer DOI
2307
BibRef
Li, Y.S.[Yong-Sen],
Meng, J.[Jiana],
Yu, Y.[Yuhai],
Wang, C.R.[Cun-Rui],
Guan, Z.Y.[Zhong-Yuan],
Image Restoration Based on Improved Generative Adversarial Networks,
ICIVC22(799-804)
IEEE DOI
2301
Deep learning, Convolution, Computational modeling,
Image edge detection, Neural networks, attention
BibRef
Lin, K.T.[Kai-Tong],
Cai, Q.L.[Qing-Ling],
Extended StyleGAN Encoder for Image Restoration,
ICPR22(2157-2164)
IEEE DOI
2212
Visualization, Codes, Noise reduction,
Generative adversarial networks, Image restoration,
Task analysis
BibRef
Mei, K.F.[Kang-Fu],
Patel, V.M.[Vishal M.],
Huang, R.[Rui],
Deep Semantic Statistics Matching (D2SM) Denoising Network,
ECCV22(VII:384-400).
Springer DOI
2211
Match statistices of noise-free data with denoised image.
BibRef
Wang, C.[Chong],
Zhang, R.[Rongkai],
Ravishankar, S.[Saiprasad],
Wen, B.[Bihan],
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for
Robust Image Restoration,
ICIP22(2886-2890)
IEEE DOI
2211
Image resolution, Inverse problems, Supervised learning,
Reinforcement learning, Markov processes, Robustness, Denoiser prior
BibRef
Li, F.[Fei],
Shen, L.F.[Ling-Feng],
Mi, Y.[Yang],
Li, Z.B.[Zhen-Bo],
DRCNet: Dynamic Image Restoration Contrastive Network,
ECCV22(XIX:514-532).
Springer DOI
2211
BibRef
Liu, X.W.[Xiong-Wei],
Sheng, Z.[Zehua],
Shen, H.L.[Hui-Liang],
Frequency-Relevant Residual Learning for Multi-Modal Image Denoising,
ICIP22(86-90)
IEEE DOI
2211
Visualization, Tensors, Frequency-domain analysis, Noise reduction,
Prediction algorithms, Image restoration, Task analysis, spatial reconstruction
BibRef
Wang, Z.D.[Zhen-Dong],
Cun, X.D.[Xiao-Dong],
Bao, J.M.[Jian-Min],
Zhou, W.G.[Wen-Gang],
Liu, J.Z.[Jian-Zhuang],
Li, H.Q.[Hou-Qiang],
Uformer: A General U-Shaped Transformer for Image Restoration,
CVPR22(17662-17672)
IEEE DOI
2210
Modulation, Transformer cores, Transformers, Image restoration,
Decoding, Computational photography, Low-level vision
BibRef
Lee, W.[Wooseok],
Son, S.[Sanghyun],
Lee, K.M.[Kyoung Mu],
AP-BSN: Self-Supervised Denoising for Real-World Images via
Asymmetric PD and Blind-Spot Network,
CVPR22(17704-17713)
IEEE DOI
2210
Training, Photography, Visualization, Image resolution, Correlation,
Noise reduction, Computational photography,
Self- semi- meta- unsupervised learning
BibRef
Neshatavar, R.[Reyhaneh],
Yavartanoo, M.[Mohsen],
Son, S.[Sanghyun],
Lee, K.M.[Kyoung Mu],
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image
Denoising by Disentangling Noise from Image,
CVPR22(17562-17570)
IEEE DOI
2210
Training, Correlation, Codes, Noise reduction,
Noise measurement, Low-level vision, Emergency Reviews,
Image and video synthesis and generation
BibRef
Mou, C.[Chong],
Wang, Q.[Qian],
Zhang, J.[Jian],
Deep Generalized Unfolding Networks for Image Restoration,
CVPR22(17378-17389)
IEEE DOI
2210
Degradation, Deep learning, Neural networks, Estimation,
Iterative algorithms, Image restoration, Low-level vision,
Image and video synthesis and generation
BibRef
Wang, W.X.[Wei-Xi],
Li, J.[Ji],
Ji, H.[Hui],
Self-supervised Deep Image Restoration via Adaptive Stochastic
Gradient Langevin Dynamics,
CVPR22(1979-1988)
IEEE DOI
2210
Deep learning, Costs, Image restoration, Computational efficiency,
Bayes methods, Low-level vision,
Self- semi- meta- unsupervised learning
BibRef
Kim, H.[Heewon],
Baik, S.[Sungyong],
Choi, M.[Myungsub],
Choi, J.[Janghoon],
Lee, K.M.[Kyoung Mu],
Searching for Controllable Image Restoration Networks,
ICCV21(14214-14223)
IEEE DOI
2203
Image quality, Degradation, Adaptation models, Costs, Modulation,
Graphics processing units,
Efficient training and inference methods
BibRef
Jo, Y.[Yeonsik],
Chun, S.Y.[Se Young],
Choi, J.H.[Jong-Hyun],
Rethinking Deep Image Prior for Denoising,
ICCV21(5067-5076)
IEEE DOI
2203
Inverse problems, Gaussian noise, Noise reduction, Fitting, Manuals,
Noise measurement, Low-level and physics-based vision,
BibRef
Jang, G.[Geonwoon],
Lee, W.[Wooseok],
Son, S.[Sanghyun],
Lee, K.[Kyoungmu],
C2N: Practical Generative Noise Modeling for Real-World Denoising,
ICCV21(2330-2339)
IEEE DOI
2203
Correlation, Image synthesis, Noise reduction, Benchmark testing,
Generators, Noise generators, Computational photography,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Mou, C.[Chong],
Zhang, J.[Jian],
Wu, Z.Y.[Zhuo-Yuan],
Dynamic Attentive Graph Learning for Image Restoration,
ICCV21(4308-4317)
IEEE DOI
2203
Adaptation models, Visualization, Correlation, Image coding,
Computational modeling, Message passing,
BibRef
Abu-Hussein, S.[Shady],
Tirer, T.[Tom],
Chun, S.Y.[Se Young],
Eldar, Y.C.[Yonina C.],
Giryes, R.[Raja],
Image Restoration by Deep Projected GSURE,
WACV22(91-100)
IEEE DOI
2202
Training, Learning systems, Inverse problems, Superresolution,
Neural networks, Image restoration, Image Processing
BibRef
Sarker, K.[Krishanu],
Yang, X.L.[Xiu-Long],
Li, Y.[Yang],
Belkasim, S.[Saeid],
Ji, S.H.[Shi-Hao],
A Unified Density-Driven Framework for Effective Data Denoising and
Robust Abstention,
ICIP21(594-598)
IEEE DOI
2201
Training, Deep learning, Uncertainty, Filtering, Image processing,
Data integrity, Noise reduction
BibRef
Zhang, R.K.[Rong-Kai],
Zhu, J.[Jiang],
Zha, Z.Y.[Zhi-Yuan],
Dauwels, J.[Justin],
Wen, B.[Bihan],
R3L: Connecting Deep Reinforcement Learning To Recurrent Neural
Networks for Image Denoising Via Residual Recovery,
ICIP21(1624-1628)
IEEE DOI
2201
Training, Recurrent neural networks, Supervised learning,
Reinforcement learning, Benchmark testing, Search problems,
Residual Recovery
BibRef
Zheng, H.Y.[Hong-Yi],
Yong, H.W.[Hong-Wei],
Zhang, L.[Lei],
Deep Convolutional Dictionary Learning for Image Denoising,
CVPR21(630-641)
IEEE DOI
2111
Convolutional codes, Measurement, Deep learning, Adaptation models,
Visualization, Dictionaries, Noise reduction
BibRef
Huang, T.[Tao],
Li, S.J.[Song-Jiang],
Jia, X.[Xu],
Lu, H.C.[Hu-Chuan],
Liu, J.Z.[Jian-Zhuang],
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images,
CVPR21(14776-14785)
IEEE DOI
2111
Training, Noise reduction, Neural networks,
Network architecture, Benchmark testing
BibRef
Tachella, J.[Julián],
Tang, J.Q.[Jun-Qi],
Davies, M.[Mike],
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters,
CVPR21(8614-8623)
IEEE DOI
2111
Training, Adaptation models, Noise reduction, Imaging, Tools,
Information filters, Filtering theory
BibRef
Ren, C.[Chao],
He, X.[Xiaohai],
Wang, C.C.[Chun-Cheng],
Zhao, Z.B.[Zhi-Bo],
Adaptive Consistency Prior based Deep Network for Image Denoising,
CVPR21(8592-8602)
IEEE DOI
2111
Maximum a posteriori estimation, Adaptation models,
Filtering, Noise reduction, Reliability
BibRef
Kapishnikov, A.[Andrei],
Venugopalan, S.[Subhashini],
Avci, B.[Besim],
Wedin, B.[Ben],
Terry, M.[Michael],
Bolukbasi, T.[Tolga],
Guided Integrated Gradients:
An Adaptive Path Method for Removing Noise,
CVPR21(5048-5056)
IEEE DOI
2111
Measurement, Deep learning, Adaptation models, Visualization,
Computational modeling, Predictive models
BibRef
Pang, T.Y.[Tong-Yao],
Zheng, H.[Huan],
Quan, Y.H.[Yu-Hui],
Ji, H.[Hui],
Recorrupted-to-Recorrupted:
Unsupervised Deep Learning for Image Denoising,
CVPR21(2043-2052)
IEEE DOI
2111
Training, Deep learning, AWGN, Noise reduction,
Cost function
BibRef
Calvarons, A.F.[Adria Font],
Improved Noise2Noise Denoising with Limited Data,
NTIRE21(796-805)
IEEE DOI
2109
Training, Limiting, Image color analysis, Noise reduction,
Training data, Network architecture
BibRef
Muller, L.K.[Lorenz K.],
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast
Neural Image Enhancement,
NTIRE21(284-293)
IEEE DOI
2109
Training, Convolution, Noise reduction,
Cameras, Energy efficiency, Sensors
BibRef
Chu, X.J.[Xiao-Jie],
Chen, L.Y.[Liang-Yu],
Chen, C.P.[Cheng-Peng],
Lu, X.[Xin],
Improving Image Restoration by Revisiting Global Information
Aggregation,
ECCV22(VII:53-71).
Springer DOI
2211
BibRef
Chen, L.Y.[Liang-Yu],
Lu, X.[Xin],
Zhang, J.[Jie],
Chu, X.J.[Xiao-Jie],
Chen, C.P.[Cheng-Peng],
HINet: Half Instance Normalization Network for Image Restoration,
NTIRE21(182-192)
IEEE DOI
2109
Visualization, Transform coding,
Image restoration, Task analysis
BibRef
Tran, L.D.[Linh Duy],
Nguyen, S.M.[Son Minh],
Arai, M.[Masayuki],
GAN-based Noise Model for Denoising Real Images,
ACCV20(IV:560-572).
Springer DOI
2103
BibRef
Zhang, M.,
Shi, Y.,
Sun, X.,
Ling, N.,
Qi, N.,
Learning Redundant Sparsifying Transform based on Equi-Angular Frame,
VCIP20(439-442)
IEEE DOI
2102
Transforms, Computational modeling, Image denoising,
Adaptation models, Signal processing algorithms,
mutual coherence
BibRef
Wang, H.X.[Hai-Xin],
Zhang, T.H.[Tian-Hao],
Yu, M.[Muzhi],
Sun, J.[Jinan],
Ye, W.[Wei],
Wang, C.[Chen],
Zhang, S.K.[Shi-Kun],
Stacking Networks Dynamically for Image Restoration Based on the
Plug-and-play Framework,
ECCV20(XIII:446-462).
Springer DOI
2011
BibRef
Kong, L.Y.[Ling-Yi],
Ding, D.D.[Dan-Dan],
Liu, F.C.[Fu-Chang],
Mukherjee, D.[Debargha],
Joshi, U.[Urvang],
Chen, Y.[Yue],
Guided CNN Restoration with Explicitly Signaled Linear Combination,
ICIP20(3379-3383)
IEEE DOI
2011
Encoding, Image restoration, Training, Bit rate, Adaptation models,
Video coding, Tools, CNN, AV1, video coding, image restoration, in-loop filter
BibRef
Kattakinda, P.,
Rajagopalan, A.N.,
Unpaired Image Denoising,
ICIP20(1073-1077)
IEEE DOI
2011
Noise measurement, Image denoising, Training, Noise reduction,
Noise level, Jacobian matrices, Image restoration, image denoising,
unsupervised methods
BibRef
Mamiya, K.,
Miyata, T.,
Few-Class Learning For Image-Classification-Aware Denoising,
ICIP20(948-952)
IEEE DOI
2011
Noise reduction, Training, Noise measurement, Image denoising,
Noise level, Task analysis, Entropy, image denoising, fine-tuning
BibRef
He, J.W.[Jing-Wen],
Dong, C.[Chao],
Qiao, Y.[Yu],
Interactive Multi-dimension Modulation with Dynamic Controllable
Residual Learning for Image Restoration,
ECCV20(XX:53-68).
Springer DOI
2011
BibRef
Yue, Z.S.[Zong-Sheng],
Zhao, Q.[Qian],
Zhang, L.[Lei],
Meng, D.Y.[De-Yu],
Dual Adversarial Network: Toward Real-world Noise Removal and Noise
Generation,
ECCV20(X:41-58).
Springer DOI
2011
BibRef
Wang, Y.Z.[Yu-Zhi],
Huang, H.B.[Hai-Bin],
Xu, Q.[Qin],
Liu, J.M.[Jia-Ming],
Liu, Y.Q.[Yi-Qun],
Wang, J.[Jue],
Practical Deep Raw Image Denoising on Mobile Devices,
ECCV20(VI:1-16).
Springer DOI
2011
BibRef
Wu, X.H.[Xiao-He],
Liu, M.[Ming],
Cao, Y.[Yue],
Ren, D.W.[Dong-Wei],
Zuo, W.M.[Wang-Meng],
Unpaired Learning of Deep Image Denoising,
ECCV20(IV:352-368).
Springer DOI
2011
BibRef
Anwar, S.,
Huynh, C.P.,
Porikli, F.M.,
Identity Enhanced Residual Image Denoising,
NTIRE20(2201-2210)
IEEE DOI
2008
Noise reduction, Convolution, Image denoising, Noise measurement,
Neurons, Convolutional neural networks, Training
BibRef
Liu, Y.,
Anwar, S.,
Zheng, L.,
Tian, Q.,
GradNet Image Denoising,
NTIRE20(2140-2149)
IEEE DOI
2008
Image edge detection, Noise reduction, Feature extraction,
Noise measurement, Image denoising, Machine learning, Task analysis
BibRef
Du, W.C.[Wen-Chao],
Chen, H.[Hu],
Yang, H.Y.[Hong-Yu],
Learning Invariant Representation for Unsupervised Image Restoration,
CVPR20(14471-14480)
IEEE DOI
2008
Image restoration, Image reconstruction, Task analysis, Semantics,
Noise measurement, Training, Robustness
BibRef
Vaksman, G.,
Elad, M.,
Milanfar, P.,
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation,
NTIRE20(2220-2229)
IEEE DOI
2008
Noise reduction, Transforms, Training, Noise measurement,
Image denoising, Adaptation models, Image restoration
BibRef
Maeda, S.,
Fast and Flexible Image Blind Denoising via Competition of Experts,
NTIRE20(2239-2247)
IEEE DOI
2008
Noise reduction, Training, Noise measurement, Task analysis,
Learning systems, Noise level, Computational efficiency
BibRef
Moran, N.,
Schmidt, D.,
Zhong, Y.,
Coady, P.,
Noisier2Noise: Learning to Denoise From Unpaired Noisy Data,
CVPR20(12061-12069)
IEEE DOI
2008
Noise measurement, Training, Noise reduction, Standards,
Neural networks, Image denoising, Image reconstruction
BibRef
Gu, S.,
Li, Y.,
Van Gool, L.J.,
Timofte, R.,
Self-Guided Network for Fast Image Denoising,
ICCV19(2511-2520)
IEEE DOI
2004
feature extraction, image denoising, image resolution,
image restoration, neural nets, PSNR, self-guided network,
Image denoising
BibRef
Izadi, S.,
Mirikharaji, Z.,
Zhao, M.,
Hamarneh, G.,
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors,
VRMI19(476-484)
IEEE DOI
2004
Noise measurement, Image denoising, Noise reduction,
Image reconstruction, Training, Biomedical imaging, deep learning,
blind image denoising
BibRef
Brooks, T.[Tim],
Mildenhall, B.[Ben],
Xue, T.F.[Tian-Fan],
Chen, J.W.[Jia-Wen],
Sharlet, D.[Dillon],
Barron, J.T.[Jonathan T.],
Unprocessing Images for Learned Raw Denoising,
CVPR19(11028-11037).
IEEE DOI
2002
For code:
See also Image Unprocessing: A Pipeline to Recover Raw Data from sRGB Images.
BibRef
Krull, A.[Alexander],
Buchholz, T.O.[Tim-Oliver],
Jug, F.[Florian],
Noise2Void - Learning Denoising From Single Noisy Images,
CVPR19(2124-2132).
IEEE DOI
2002
BibRef
Zou, H.,
Lan, R.,
Zhong, Y.,
Liu, Z.,
Luo, X.,
EDCNN: A Novel Network for Image Denoising,
ICIP19(1129-1133)
IEEE DOI
1910
Image denoising, residual learning, convolutional neural network,
residual excitation
BibRef
Mukherjee, S.[Subhayan],
Kottayil, N.K.[Navaneeth Kamballur],
Sun, X.Y.[Xin-Yao],
Cheng, I.[Irene],
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter
Performance,
ICIAR19(I:112-125).
Springer DOI
1909
BibRef
Ren, H.Y.[Hao-Yu],
El-khamy, M.[Mostafa],
Lee, J.[Jungwon],
DN-ResNet: Efficient Deep Residual Network for Image Denoising,
ACCV18(V:215-230).
Springer DOI
1906
BibRef
Yu, K.,
Dong, C.,
Lin, L.,
Loy, C.C.,
Crafting a Toolchain for Image Restoration by Deep Reinforcement
Learning,
CVPR18(2443-2452)
IEEE DOI
1812
Image restoration, Tools, Distortion, Task analysis,
Transform coding, Complexity theory
BibRef
Lefkimmiatis, S.,
Universal Denoising Networks:
A Novel CNN Architecture for Image Denoising,
CVPR18(3204-3213)
IEEE DOI
1812
Image restoration, Noise level, Distortion, Image denoising,
Training, Noise reduction, Transforms
BibRef
Ryu, J.,
Kim, Y.,
Conditional Distribution Learning with Neural Networks and its
Application to Universal Image Denoising,
ICIP18(3214-3218)
IEEE DOI
1809
Neural networks, Noise reduction, Noise measurement,
Context modeling, Gray-scale, Boats, Training, Universal denoising,
plug-in approach
BibRef
Song, P.,
Rodrigues, M.R.D.,
Multimodal Image Denoising Based on Coupled Dictionary Learning,
ICIP18(515-519)
IEEE DOI
1809
Dictionaries, Image denoising, Machine learning, Training,
Noise reduction, Noise measurement, Image reconstruction,
guidance information
BibRef
Li, Y.,
Zhang, B.,
Florent, R.,
Understanding neural-network denoisers through an activation function
perspective,
ICIP17(2971-2975)
IEEE DOI
1803
Biological neural networks, Image denoising, Kernel,
Noise measurement, Noise reduction, Training, Activation function,
Neural network denoising
BibRef
Li, J.J.[Jian-Jun],
Xu, L.L.[Lan-Lan],
Li, H.J.[Hao-Jie],
Chang, C.C.[Chin-Chen],
Sun, F.M.[Fu-Ming],
Parameter Selection for Denoising Algorithms Using NR-IQA with CNN,
MMMod18(I:381-392).
Springer DOI
1802
BibRef
Gao, R.,
Grauman, K.[Kristen],
On-demand Learning for Deep Image Restoration,
ICCV17(1095-1104)
IEEE DOI
1802
convolution, image denoising, image restoration, interpolation,
learning (artificial intelligence), neural nets,
Training
BibRef
Chaudhury, S.,
Roy, H.,
Can fully convolutional networks perform well for general image
restoration problems?,
MVA17(254-257)
DOI Link
1708
Convolution, Image denoising, Image reconstruction,
Image restoration, Image segmentation, Noise measurement, Training
BibRef
Divakar, N.,
Babu, R.V.,
Image Denoising via CNNs: An Adversarial Approach,
NTIRE17(1076-1083)
IEEE DOI
1709
Feature extraction, Generators, Image denoising,
Image reconstruction, Noise measurement, Noise reduction, Training
BibRef
Koziarski, M.[Michal],
Cyganek, B.[Boguslaw],
Deep Neural Image Denoising,
ICCVG16(163-173).
Springer DOI
1611
BibRef
Mejía-Lavalle, M.[Manuel],
Ortiz, E.[Estela],
Mújica, D.[Dante],
Ruiz, J.[José],
Reyes, G.[Gerardo],
An Effective Image De-noising Alternative Approach Based on Third
Generation Neural Networks,
MCPR16(64-73).
Springer DOI
1608
BibRef
Jiang, M.Y.[Ming-Yong],
Chen, X.N.[Xiang-Ning],
Yu, X.Q.[Xia-Qiong],
Adaptive Sub-Optimal Hopfield Neural Network image restoration base on
edge detection,
IASP11(364-367).
IEEE DOI
1112
BibRef
Bernues, E.,
Cisneros, G.,
Capella, M.,
Truncated edges estimation using MLP neural nets applied to regularized
image restoration,
ICIP02(I: 341-344).
IEEE DOI
0210
BibRef
Chen, Z.Y.[Zhong-Yu],
Desai, M.,
Multiple-valued feedback neural networks for image restoration,
ICIP96(I: 753-756).
IEEE DOI
9610
BibRef
Beaudot, W.H.A.[William H.A.],
Adaptive Spatiotemporal Filtering by a Neuromorphic Model
of the Vertebrate Retina,
ICIP96(I: 427-430).
IEEE DOI
BibRef
9600
Stajniak, A.,
Szostakowski, J.,
Neural implementation of ARMA type filters for image restoration,
ICIP95(II: 520-522).
IEEE DOI
9510
BibRef
Tan, B.H.[Beng-Heok],
Wahah, A.,
Tan, E.C.[Eng-Chong],
A neural approach to optical image reconstruction,
ICIP95(II: 531-534).
IEEE DOI
9510
BibRef
Muneyasu, M.,
Yamamoto, K.,
Hinamoto, T.,
Image restoration using layered neural networks and Hopfield networks,
ICIP95(II: 33-36).
IEEE DOI
9510
BibRef
Swiniarski, R.,
Butler, M.P.,
Neural recurrent estimator to gray scale image restoration based on 2D
Kalman filtering,
ICPR92(III:425).
IEEE DOI
9208
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Noise Removal, Denoising .