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Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
“Unrolled generative adversarial networks,”
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein, · 2016
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
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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
Earlier work this paper cites.
“Image super-resolution using very deep residual channel attention networks,”
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu, · 2018
Earlier work this paper cites.
“The perception-distortion tradeoff,”
Yochai Blau and Tomer Michaeli, · 2018
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“The unreasonable effectiveness of deep features as a perceptual metric,”
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang, · 2018
Earlier work this paper cites.
“Natural and realistic single image super-resolution with explicit natural manifold discrimination,”
Jae Woong Soh, Gu Yong Park, Junho Jo, and Nam Ik Cho, · 2019
Earlier work this paper cites.
“Blind super-resolution kernel estimation using an internal-gan,”
Sefi Bell-Kligler, Assaf Shocher, and Michal Irani, · 2019
Earlier work this paper cites.
“Classification accuracy score for conditional generative models,”
Suman Ravuri and Oriol Vinyals, · 2019
Earlier work this paper cites.
“Srflow: Learning the super-resolution space with normalizing flow,”
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte, · 2020
Cited alongside, same era.
“Denoising diffusion probabilistic models,”
Jonathan Ho, Ajay Jain, and Pieter Abbeel, · 2020
Cited alongside, same era.
“Denoising diffusion implicit models,”
Jiaming Song, Chenlin Meng, and Stefano Ermon, · 2020
Cited alongside, same era.
“Hierarchical conditional flow: A unified framework for image super-resolution and image rescaling,”
Jingyun Liang, Andreas Lugmayr, Kai Zhang, Martin Danelljan, Luc Van Gool, and Radu Timofte, · 2021
Cited alongside, same era.
“Deflow: Learning complex image degradations from unpaired data with conditional flows,”
Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte, · 2021
Cited alongside, same era.
“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.
“Ms2net: Multi-scale and multi-stage feature fusion for blurred image super-resolution,”
Axi Niu, Yu Zhu, Chaoning Zhang, Jinqiu Sun, Pei Wang, In So Kweon, and Yanning Zhang, · 2022
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“Generative adversarial networks for image super-resolution: A survey,”
Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wei Lin, Wangmeng Zuo, Yanning Zhang, and Chia-Wen Lin, · 2022
Later among the works it cites.
“Deblurring via stochastic refinement,”
Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G Dimakis, and Peyman Milanfar, · 2022
Later among the works it cites.
“Image super-resolution via iterative refinement,”
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi, · 2022
Later among the works it cites.
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“Dualsr: Zero-shot dual learning for real-world super-resolution,”
Mohammad Emad, Maurice Peemen, and Henk Corporaal, · 2021
Cited alongside, same era.
“Ntire 2021 learning the super-resolution space challenge,”
Andreas Lugmayr, Martin Danelljan, and Radu Timofte, · 2021
Cited alongside, same era.
“Noise conditional flow model for learning the super-resolution space,”
Younggeun Kim and Donghee Son, · 2021
Cited alongside, same era.
“Flow-based kernel prior with application to blind super-resolution,”
Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte, · 2021
Cited alongside, same era.
“Bridging the domain gap in real world super-resolution,”
Charles Laroche and Matias Tassano, · 2022
Later among the works it cites.
“Srdiff: Single image super-resolution with diffusion probabilistic models,”
Haoying Li, Yifan Yang, Meng Chang, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen, · 2022
Later among the works it cites.
“Cold diffusion: Inverting arbitrary image transforms without noise,”
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein, · 2022
Later among the works it cites.