Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach

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Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 1914

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Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004

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Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012

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Learning a deep convolutional network for image super-resolution

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

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Adam: A method for stochastic optimization

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Diederik P Kingma and Jimmy Ba · 2014

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Very deep convolutional networks for large-scale image recognition

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Karen Simonyan and Andrew Zisserman · 2014

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Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016

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Perceptual losses for real-time style transfer and super-resolution

Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016

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Deeply-recursive convolutional network for image super-resolution

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

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

Eirikur Agustsson and Radu Timofte · 2017

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Gans trained by a two time-scale update rule converge to a local nash equilibrium

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017

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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

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Image super-resolution using dense skip connections

Tong Tong, Gen Li, Xiejie Liu, and Qinquan Gao · 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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The perception-distortion tradeoff

Yochai Blau and Tomer Michaeli · 2018

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Deep back-projection networks for super-resolution

Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2018

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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

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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

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A style-based generator architecture for generative adversarial networks

Tero Karras, Samuli Laine, and Timo Aila · 2019

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Deep network interpolation for continuous imagery effect transition

Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, and Chen Change Loy · 2019

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Image quality assessment: Unifying structure and texture similarity

Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli · 2020

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Denoising diffusion probabilistic models

Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020

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Score-based generative modeling through stochastic differential equations

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Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020

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Component divide-and-conquer for real-world image super-resolution

Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixiang Ye, Wangmeng Zuo, and Liang Lin · 2020

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Lsdir: A large scale dataset for image restoration

Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool · 2023

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Diffbir: Towards blind image restoration with generative diffusion prior

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Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Ben Fei, Bo Dai, Wanli Ouyang, Yu Qiao, and Chao Dong · 2023

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Conditional diffusion distillation

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Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M Patel, and Peyman Milanfar · 2023

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T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

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Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie · 2023

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Improving the stability of diffusion models for content consistent super-resolution