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Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware details, making them suitable for various high-level computer vision tasks as the pre-processing step.
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 1905
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Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
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Bilateral filtering for gray and color images
Carlo Tomasi and Roberto Manduchi · 1998
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A review of image denoising algorithms, with a new one
Antoni Buades, Bartomeu Coll, and Jean-Michel Morel · 2005
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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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Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen O Egiazarian · 2007
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Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space
Kostadin Dabov, Alessandro Foi, Vladmir Katkovnik, and Karen Egiazarian · 2007
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Scope of validity of psnr in image/video quality assessment
Quan Huynh-Thu and Mohammed Ghanbari · 2008
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Non-local sparse models for image restoration
Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman · 2009
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Image quality metrics: Psnr vs. ssim
Alain Hore and Djemel Ziou · 2010
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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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Making a” completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
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Structured overcomplete sparsifying transform learning with convergence guarantees and applications
B. Wen, S. Ravishankar, and Y. Bresler · 2015
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
Cited alongside, same era.
Studying very low resolution recognition using deep networks
Zhangyang Wang, Shiyu Chang, Yingzhen Yang, Ding Liu, and Thomas S Huang · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge J Belongie · 2017
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On the relation between color image denoising and classification
Jiqing Wu, Radu Timofte, Zhiwu Huang, and Luc Van Gool · 2017
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Multi-channel weighted nuclear norm minimization for real color image denoising
Jun Xu, Lei Zhang, David Zhang, and Xiangchu Feng · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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Reblur2deblur: Deblurring videos via self-supervised learning
Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, Ashok Veeraraghavan, and Jan Kautz · 2018
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
Cited alongside, same era.
Robust emotion recognition from low quality and low bit rate video: A deep learning approach
Bowen Cheng, Zhangyang Wang, Zhaobin Zhang, Zhu Li, Ding Liu, Jianchao Yang, Shuai Huang, and Thomas S Huang · 2017
Cited alongside, same era.
Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
Cited alongside, same era.
Segmentation-aware convolutional networks using local attention masks
Adam W Harley, Konstantinos G Derpanis, and Iasonas Kokkinos · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Aod-net: All-in-one dehazing network
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng · 2017
Cited alongside, same era.
Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
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A segmentation-aware deep fusion network for compressed sensing mri
Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, and John Paisley · 2018
Later among the works it cites.
Toward convolutional blind denoising of real photographs
Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, and Lei Zhang · 2018
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Focal loss for dense object detection
Tsung-Yi Lin, Priyal Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2018
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Connecting image denoising and high-level vision tasks via deep learning
Ding Liu, Bihan Wen, Jianbo Jiao, Xianming Liu, Zhangyang Wang, and Thomas S Huang · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Ziyi Shen, Wei-Sheng Lai, Tingfa Xu, Jan Kautz, and Ming-Hsuan Yang · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Benchmarking single-image dehazing and beyond
Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang · 2019
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Single image deraining: A comprehensive benchmark analysis
Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, and Xiaochun Cao · 2019
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Enhance visual recognition under adverse conditions via deep networks
Ding Liu, Bowen Cheng, Zhangyang Wang, Haichao Zhang, and Thomas S Huang · 2019
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Bridging the gap between computational photography and visual recognition
Rosaura G VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright, Pedro Davalos, Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh, Sudarshan Nagesh, et al · 2019
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