Fetching the paper…
Reading the bibliography…
The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
Earlier work this paper cites.
Referenceless image spatial quality evaluation engine
A Mittal, AK Moorthy, and AC Bovik · 2011
Earlier work this paper cites.
Making a ”completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Blind image quality evaluation using perception based features
Venkatanath N., Praneeth D., Maruthi Chandrasekhar Bh., Sumohana S. Channappayya, and Swarup S. Medasani · 2015
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
Earlier work this paper cites.
Cyclegan, a master of steganography
Casey Chu, Andrey Zhmoginov, and Mark Sandler · 2017
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
Dslr-quality photos on mobile devices with deep convolutional networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool · 2017
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, et al · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Earlier work this paper cites.
A high-quality denoising dataset for smartphone cameras
Abdelrahman Abdelhamed, Stephen Lin, and Michael S. Brown · 2018
Cited alongside, same era.
To learn image super-resolution, use a gan to learn how to do image degradation first
Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Noise2void - learning denoising from single noisy images
Alexander Krull, Tim-Oliver Buchholz, and Florian Jug · 2018
Cited alongside, same era.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang · 2018
Cited alongside, same era.
Alignflow: Cycle consistent learning from multiple domains via normalizing flows, 2019
Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, and Stefano Ermon · 2019
Later among the works it cites.
High-quality self-supervised deep image denoising, 2019
Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila · 2019
Later among the works it cites.
Unsupervised learning for real-world super-resolution
Andreas Lugmayr, Martin Danelljan, and Radu Timofte · 2019
Later among the works it cites.
Aim 2019 challenge on real-world image super-resolution: Methods and results
Andreas Lugmayr, Martin Danelljan, Radu Timofte, et al · 2019
Later among the works it cites.
Learning likelihoods with conditional normalizing flows, 2019
Christina Winkler, Daniel Worrall, Emiel Hoogeboom, and Max Welling · 2019
Later among the works it cites.
Adaflow: Domain-adaptive density estimator with application to anomaly detection and unpaired cross-domain translation, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Noise flow: Noise modeling with conditional normalizing flows
Abdelrahman Abdelhamed, Marcus A. Brubaker, and Michael S. Brown · 2019
Cited alongside, same era.
Ntire 2019 challenge on real image denoising: Methods and results
Abdelrahman Abdelhamed, Radu Timofte, Michael S. Brown, et al · 2019
Cited alongside, same era.
Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
Cited alongside, same era.
Blind super-resolution kernel estimation using an internal-gan
Sefi Bell-Kligler, Assaf Shocher, and Michal Irani · 2019
Cited alongside, same era.
Ntire 2019 challenge on real image super-resolution: Methods and results
Jianrui Cai, Shuhang Gu, Radu Timofte, and Lei Zhang · 2019
Cited alongside, same era.
Toward real-world single image super-resolution: A new benchmark and a new model
Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
Cited alongside, same era.
Masataka Yamaguchi, Yuma Koizumi, and Noboru Harada · 2019
Later among the works it cites.
Real-world super-resolution via kernel estimation and noise injection
Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang · 2020
Later among the works it cites.
Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. Prince, and M. Brubaker · 2020
Later among the works it cites.
Ntire 2020 challenge on real-world image super-resolution: Methods and results
Andreas Lugmayr, Martin Danelljan, and Radu Timofte · 2020
Later among the works it cites.
Srflow: Learning the super-resolution space with normalizing flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2020
Later among the works it cites.
Fully unsupervised probabilistic noise2void, 2020
Mangal Prakash, Manan Lalit, Pavel Tomancak, Alexander Krull, and Florian Jug · 2020
Later among the works it cites.
Unsupervised real-world image super resolution via domain-distance aware training, 2020
Yunxuan Wei, Shuhang Gu, Yawei Li, and Longcun Jin · 2020
Later among the works it cites.
Unpaired learning of deep image denoising, 2020
Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, and Wangmeng Zuo · 2020
Later among the works it cites.