5.3.8 Neural Networks for Noise Removal, Denoising, Restoration

Chapter Contents (Back)
Neural Networks. Learning. Noise Removal. Denoising.

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
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And:
Adaptive discontinuity location in image restoration,
ICIP94(II: 665-669).
IEEE DOI 9411
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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
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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
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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
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Wang, Y., Wahl, F.M.,
Multiobjective Neural Network for Image Reconstruction,
VISP(144), No. 4, August 1997, pp. 233-236. 9806
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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
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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
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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
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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
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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
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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
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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
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Lyu, G.H.[Guo-Hao], Yin, H.[Hui], Yu, X.[Xinyan], 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, Pattern recognition 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 Real Image Restoration and Enhancement,
ECCV20(XXV:492-511).
Springer DOI 2011
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

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

Li, K.Y.[Kai-Yan], Zhou, W.M.[Wei-Min], Li, H.[Hua], Anastasio, M.A.[Mark A.],
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks,
MedImg(40), No. 9, September 2021, pp. 2295-2305.
IEEE DOI 2109
Observers, Noise reduction, Task analysis, Signal detection, Biomedical imaging, Image denoising, PSNR, Image denoising, deep learning 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.[Zhengyong], He, X.[Xiaohai], Qing, L.[Linbo],
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.[Xixi],
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.[Lintao], 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.[Dongyuan], 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.[Faqiang], 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

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

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


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.[Zhenbo],
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.[Jianmin], 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, Pattern recognition, 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, Pattern recognition, 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, Pattern recognition, 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, Pattern recognition, 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.[Rongkai], Zhu, J.[Jiang], Zha, Z.[Zhiyuan], 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, Pattern recognition 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, Pattern recognition, 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

Ye, N.Z.[Nian-Zjin], 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, Pattern recognition, Computational efficiency BibRef

Pang, T.Y.[Tong-Yao], Zheng, H.[Huan], Quan, Y.[Yuhui], 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, Pattern recognition 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, Pattern recognition 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, Pattern recognition, Task analysis BibRef

Achddou, R.[Raphaël], Gousseau, Y.[Yann], Ladjal, S.[Saïd],
Synthetic Images as a Regularity Prior for Image Restoration Neural Networks,
SSVM21(333-345).
Springer DOI 2106
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, 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, 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

Ignatov, A., Van Gool, L.J., Timofte, R.,
Replacing Mobile Camera ISP with a Single Deep Learning Model,
NTIRE20(2275-2285)
IEEE DOI 2008
Cameras, Task analysis, Image color analysis, Machine learning, Smart phones, Image restoration, Image resolution 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
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.[Xinyao], 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,
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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).
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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).
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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
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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
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Chen, Z.Y.[Zhong-Yu], Desai, M.,
Multiple-valued feedback neural networks for image restoration,
ICIP96(I: 753-756).
IEEE DOI 9610
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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
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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
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Muneyasu, M., Yamamoto, K., Hinamoto, T.,
Image restoration using layered neural networks and Hopfield networks,
ICIP95(II: 33-36).
IEEE DOI 9510
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Swiniarski, R., Butler, M.P.,
Neural recurrent estimator to gray scale image restoration based on 2D Kalman filtering,
ICPR92(III:425).
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Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Noise Removal, Denoising .


Last update:Dec 4, 2022 at 15:58:45