Fetching the paper…
Reading the bibliography…
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
When is speckle noise multiplicative?
Moshe Tur, Kuen-Chang Chin, and Joseph W Goodman · 1982
Earlier work this paper cites.
Kodak lossless true color image suite
Rich Franzen · 1999
Earlier work this paper cites.
Speckle noise and the detection of faint companions
René Racine, Gordon AH Walker, Daniel Nadeau, René Doyon, and Christian Marois · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
What is the space of camera response functions?
Michael D Grossberg and Shree K Nayar · 2003
Earlier work this paper cites.
A non-local algorithm for image denoising
Antoni Buades, Bartomeu Coll, and Jean-Michel Morel · 2005
Earlier work this paper cites.
Image denoising by sparse 3-D transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
Earlier work this paper cites.
Is denoising dead?
Priyam Chatterjee and Peyman Milanfar · 2009
Earlier work this paper cites.
Non-local sparse models for image restoration
Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman · 2009
Earlier work this paper cites.
Fields of experts
Stefan Roth and Michael J Black · 2009
Earlier work this paper cites.
Fast image recovery using variable splitting and constrained optimization
Manya V Afonso, José M Bioucas-Dias, and Mário AT Figueiredo · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein · 2011
Earlier work this paper cites.
Learning non-local range markov random field for image restoration
Jian Sun and Marshall F Tappen · 2011
Earlier work this paper cites.
Color demosaicking by local directional interpolation and nonlocal adaptive thresholding
Lei Zhang, Xiaolin Wu, Antoni Buades, and Xin Li · 2011
Earlier work this paper cites.
Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
Earlier work this paper cites.
Learning how to combine internal and external denoising methods
Harold Christopher Burger, Christian Schuler, and Stefan Harmeling · 2013
Earlier work this paper cites.
Weighted nuclear norm minimization with application to image denoising
Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng · 2014
Earlier work this paper cites.
Shrinkage fields for effective image restoration
Uwe Schmidt and Stefan Roth · 2014
Earlier work this paper cites.
Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
The noise clinic: a blind image denoising algorithm
Marc Lebrun, Miguel Colom, and Jean-Michel Morel · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Blind image quality evaluation using perception based features
N Venkatanath, D Praneeth, Maruthi Chandrasekhar Bh, Sumohana S Channappayya, and Swarup S Medasani · 2015
Cited alongside, same era.
Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Yunjin Chen and Thomas Pock · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A holistic approach to cross-channel image noise modeling and its application to image denoising
Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, and Seon Joo Kim · 2016
Cited alongside, same era.
Dilated residual networks with symmetric skip connection for image denoising
Yali Peng, Lu Zhang, Shigang Liu, Xiaojun Wu, Yu Zhang, and Xili Wang · 2019
Later among the works it cites.
Variational denoising network: Toward blind noise modeling and removal
Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang, and Deyu Meng · 2019
Later among the works it cites.
Residual non-local attention networks for image restoration
Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu · 2019
Later among the works it cites.
Image denoising using deep cnn with batch renormalization
Chunwei Tian, Yong Xu, and Wangmeng Zuo · 2020
Later among the works it cites.
Residual dense network for image restoration
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2020
Later among the works it cites.
Pre-trained image processing transformer
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Cited alongside, same era.
A plug-and-play priors approach for solving nonlinear imaging inverse problems
Ulugbek S Kamilov, Hassan Mansour, and Brendt Wohlberg · 2017
Cited alongside, same era.
Non-local color image denoising with convolutional neural networks
Stamatios Lefkimmiatis · 2017
Cited alongside, same era.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Cited alongside, same era.
Learning a no-reference quality metric for single-image super-resolution
Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang · 2017
Cited alongside, same era.
Waterloo exploration database: New challenges for image quality assessment models
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang · 2017
Cited alongside, same era.
Benchmarking denoising algorithms with real photographs
Tobias Plotz and Stefan Roth · 2017
Cited alongside, same era.
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
Later among the works it cites.
Towards flexible blind JPEG artifacts removal
Jiaxi Jiang, Kai Zhang, and Radu Timofte · 2021
Later among the works it cites.
Bossnas: Exploring hybrid cnn-transformers with block-wisely self-supervised neural architecture search
Changlin Li, Tao Tang, Guangrun Wang, Jiefeng Peng, Bing Wang, Xiaodan Liang, and Xiaojun Chang · 2021
Later among the works it cites.
LocalViT: Bringing locality to vision transformers
Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool · 2021
Later among the works it cites.
Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Dynamic attentive graph learning for image restoration
Chong Mou, Jian Zhang, and Zhuoyuan Wu · 2021
Later among the works it cites.
Adaptive consistency prior based deep network for image denoising
Chao Ren, Xiaohai He, Chuncheng Wang, and Zhibo Zhao · 2021
Later among the works it cites.
Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
Later among the works it cites.
Incorporating convolution designs into visual transformers
Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu · 2021
Later among the works it cites.
Plug-and-play image restoration with deep denoiser prior
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
Designing a practical degradation model for deep blind image super-resolution
Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
CMT: Convolutional neural networks meet vision transformers
Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu · 2022
Closest in time.
Uformer: A general u-shaped transformer for image restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li · 2022
Closest in time.
Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2022
Closest in time.
Swin-unet: Unet-like pure transformer for medical image segmentation
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2023
Closest in time.