Explorable Super Resolution

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

David Martin, Charless Fowlkes, Doron Tal, Jitendra Malik, et al · 2001

Earlier work this paper cites.

Optimization techniques in modern sampling theory

Tomer Michaeli and Yonina C. Eldar · 2010

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.

Nonparametric blind super-resolution

Tomer Michaeli and Michal Irani · 2013

Earlier work this paper cites.

Learning a deep convolutional network for image super-resolution

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014

Earlier work this paper cites.

Generative adversarial nets

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014

Earlier work this paper cites.

Deep multi-scale video prediction beyond mean square error

Original

Michael Mathieu, Camille Couprie, and Yann LeCun · 2015

Earlier work this paper cites.

Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016

Earlier work this paper cites.

Image-to-image translation with conditional adversarial networks

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016

Earlier work this paper cites.

Accurate image super-resolution using very deep convolutional networks

Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016

Earlier work this paper cites.

Invertible conditional gans for image editing

Original

Guim Perarnau, Joost Van De Weijer, Bogdan Raducanu, and Jose M Álvarez · 2016

Earlier work this paper cites.

Generative visual manipulation on the natural image manifold

Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016

Cited alongside, same era.

Ntire 2017 challenge on single image super-resolution: Dataset and study

Eirikur Agustsson and Radu Timofte · 2017

Cited alongside, same era.

Improved training of wasserstein gans

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017

Cited alongside, same era.

Photo-realistic single image super-resolution using a generative adversarial network

Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 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.

Enhancenet: Single image super-resolution through automated texture synthesis

Multi–scale recursive and perception–distortion controllable image super–resolution

Pablo Navarrete Michelini, Dan Zhu, and Hanwen Liu · 2018

Later among the works it cites.

“zero-shot” super-resolution using deep internal learning

Assaf Shocher, Nadav Cohen, and Michal Irani · 2018

Later among the works it cites.

Recovering realistic texture in image super-resolution by deep spatial feature transform

Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy · 2018

Later among the works it cites.

Esrgan: Enhanced super-resolution generative adversarial networks

Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018

Later among the works it cites.

A fully progressive approach to single-image super-resolution

Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, and Christopher Schroers · 2018

Later among the works it cites.