Fetching the paper…
Reading the bibliography…
Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image corresponds to multiple HR images.
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.
Super resolution for remote sensing images based on a universal hidden markov tree model
Feng Li, Xiuping Jia, Donald Fraser, and Andrew Lambert · 2009
Earlier work this paper cites.
Evaluation of image resolution and super-resolution on face recognition performance
Clinton Fookes, Frank Lin, Vinod Chandran, and Sridha Sridharan · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
Earlier work this paper cites.
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
Mehdi SM Sajjadi, Bernhard Scholkopf, and Michael Hirsch · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Generative adversarial network-based image super-resolution using perceptual content losses
Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi, and Jong-Seok Lee · 2018
Cited alongside, same era.
Ntire 2018 challenge on single image super-resolution: methods and results
Radu Timofte, Shuhang Gu, Jiqing Wu, and Luc Van Gool · 2018
Cited alongside, same era.
Esrgan: Enhanced super-resolution generative adversarial networks
Embedded block residual network: A recursive restoration model for single-image super-resolution
Yajun Qiu, Ruxin Wang, Dapeng Tao, and Jun Cheng · 2019
Later among the works it cites.
Srobb: Targeted perceptual loss for single image super-resolution
Mohammad Saeed Rad, Behzad Bozorgtabar, Urs-Viktor Marti, Max Basler, Hazim Kemal Ekenel, and Jean-Philippe Thiran · 2019
Later among the works it cites.
Ranksrgan: Generative adversarial networks with ranker for image super-resolution
Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao · 2019
Later among the works it cites.
Closed-loop matters: Dual regression networks for single image super-resolution
Yong Guo, Jian Chen, Jingdong Wang, Qi Chen, Jiezhang Cao, Zeshuai Deng, Yanwu Xu, and Mingkui Tan · 2020
Later among the works it cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
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.
Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
De novo-designed near-infrared nanoaggregates for super-resolution monitoring of lysosomes in cells, in whole organoids, and in vivo
Hongbao Fang, Shankun Yao, Qixin Chen, Chunyan Liu, Yuqi Cai, Shanshan Geng, Yang Bai, Zhiqi Tian, Amanda L Zacharias, Takanori Takebe, et al · 2019
Cited alongside, same era.
Progressive face super-resolution via attention to facial landmark
Deokyun Kim, Minseon Kim, Gihyun Kwon, and Dae-Shik Kim · 2019
Cited alongside, same era.
Mish: A self regularized non-monotonic neural activation function
Diganta Misra · 2019
Cited alongside, same era.
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 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.
Self-controllable super-resolution deep learning framework for surveillance drones in security applications
Soohyun Park, Yeongeun Kang, Jeman Park, and Joongheon Kim · 2020
Later among the works it cites.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
Closest in time.
Improved denoising diffusion probabilistic models, 2021
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Closest in time.