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Recovering an image from a noisy observation is a key problem in signal processing.
“Nonlinear total variation based noise removal algorithms,”
Leonid I. Rudin, Stanley Osher, and Emad Fatemi, · 1992
Earlier work this paper cites.
“Image denoising via sparse and redundant representations over learned dictionaries,”
Michael Elad and Michal Aharon, · 2006
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“Image denoising by sparse 3-D transform-domain collaborative filtering,”
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, · 2007
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“Contour detection and hierarchical image segmentation,”
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik, · 2011
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“Image denoising: Can plain neural networks compete with BM3D?,”
Harold C. Burger, Christian J. Schuler, and Stefan Harmeling, · 2012
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“The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,”
David Shuman, Sunil Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst, · 2013
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“Weighted nuclear norm minimization with application to image denoising,”
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng, · 2014
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“Going deeper with convolutions,”
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, · 2015
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“An exploration of parameter redundancy in deep networks with circulant projections,”
Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, and Shi-Fu Chang, · 2015
Cited alongside, same era.
“Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,”
Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang, · 2016
Cited alongside, same era.
“Compression of fully-connected layer in neural network by kronecker product,”
J. Wu, · 2016
Cited alongside, same era.
“Graph Laplacian regularization for image denoising: analysis in the continuous domain,”
Jiahao Pang and Gene Cheung, · 2017
Cited alongside, same era.
“Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising,”
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang, · 2017
Cited alongside, same era.
“Image denoising via CNNs: an adversarial approach,”
Nithish Divakar and R. Venkatesh Babu, · 2017
Later among the works it cites.
“Universal denoising networks: a novel CNN architecture for image denoising,”
Stamatios Lefkimmiatis, · 2018
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“Noise2Noise: learning image restoration without clean data,”
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila, · 2018
Later among the works it cites.
“Denoising prior driven deep neural network for image restoration,”
Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, and Xiaotong Lu, · 2018
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“Image blind denoising with generative adversarial network based noise modeling,”
Jingwen Chen, Jiawei Chen, Hongyang Chao, and Ming Yang, · 2018
Later among the works it cites.
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“Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification,”
Woong Bae, Jae Jun Yoo, and Jong Chul Ye, · 2017
Cited alongside, same era.
“Dynamic edge-conditioned filters in convolutional neural networks on graphs,”
Martin Simonovsky and Nikos Komodakis, · 2017
Cited alongside, same era.
“Nonlocality-reinforced convolutional neural networks for image denoising,”
Cristovao Cruz, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian, · 2018
Later among the works it cites.
“Sampling of graph signals via randomized local aggregations,”
Diego Valsesia, Giulia Fracastoro, and Enrico Magli, · 2019
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