Fetching the paper…
Reading the bibliography…
Diffusion models in image Super-Resolution (SR) treat all image regions uniformly, which risks compromising the overall image quality by potentially introducing artifacts during denoising of less-complex regions.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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 unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Raisr: rapid and accurate image super resolution
Yaniv Romano, John Isidoro, and Peyman Milanfar · 2016
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.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Earlier work this paper cites.
Ntire 2018 challenge on single image super-resolution: Methods and results
Radu Timofte, Shuhang Gu, Jiqing Wu, and Luc Van Gool · 2018
Earlier work this paper cites.
Recovering realistic texture in image super-resolution by deep spatial feature transform
Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy · 2018
Earlier work this paper 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
Earlier work this paper cites.
Crafting a toolchain for image restoration by deep reinforcement learning
Ke Yu, Chao Dong, Liang Lin, and Chen Change Loy · 2018
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Deep progressive convolutional neural network for blind super-resolution with multiple degradations
Jun Xiao, Rui Zhao, Shun-Cheung Lai, Wenqi Jia, and Kin-Man Lam · 2019
Earlier work this paper cites.
Ranksrgan: Generative adversarial networks with ranker for image super-resolution
Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao · 2019
Earlier work this paper cites.
Densely residual laplacian super-resolution
Saeed Anwar and Nick Barnes · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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
Earlier work this paper cites.
Learned image downscaling for upscaling using content adaptive resampler
Wanjie Sun and Zhenzhong Chen · 2020
Earlier work this paper cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Cited alongside, same era.
Learning continuous image representation with local implicit image function
Yinbo Chen, Sifei Liu, and Xiaolong Wang · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Cited alongside, same era.
Adversarial text-to-image synthesis: A review
Stanislav Frolov, Tobias Hinz, Federico Raue, Jörn Hees, and Andreas Dengel · 2021
Cited alongside, same era.
Visualization of supervised and self-supervised neural networks via attribution guided factorization
Shir Gur, Ameen Ali, and Lior Wolf · 2021
Cited alongside, same era.
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.
Variable rate allocation for vector-quantized autoencoders
Federico Baldassarre, Alaaeldin El-Nouby, and Hervé Jégou · 2023
Closest in time.
Multidiffusion: Fusing diffusion paths for controlled image generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel · 2023
Closest in time.
Vision transformers need registers
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski · 2023
Closest in time.
Inversion by direct iteration: An alternative to denoising diffusion for image restoration
Mauricio Delbracio and Peyman Milanfar · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Self-feature learning: An efficient deep lightweight network for image super-resolution
Jun Xiao, Qian Ye, Rui Zhao, Kin-Man Lam, and Kao Wan · 2021
Cited alongside, same era.
Path-restore: Learning network path selection for image restoration
Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, and Chen Change Loy · 2021
Cited alongside, same era.
Perception prioritized training of diffusion models. 2022 ieee
Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, H Kim, and S Yoon · 2022
Cited alongside, same era.
Mr image denoising and super-resolution using regularized reverse diffusion
Hyungjin Chung, Eun Sun Lee, and Jong Chul Ye · 2022
Cited alongside, same era.
Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction
Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye · 2022
Cited alongside, same era.
Lar-sr: A local autoregressive model for image super-resolution
Baisong Guo, Xiaoyun Zhang, Haoning Wu, Yu Wang, Ya Zhang, and Yan-Feng Wang · 2022
Cited alongside, same era.
Not all patches are equal: Hierarchical dataset condensation for single image super-resolution
Qingtang Ding, Zhengyu Liang, Longguang Wang, Yingqian Wang, and Jungang Yang · 2023
Closest in time.
Demofusion: Democratising high-resolution image generation with no $$$
Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, and Zhanyu Ma · 2023
Closest in time.
Single image super-resolution approaches in medical images based-deep learning: a survey
Walid El-Shafai, Anas M Ali, Samy Abd El-Nabi, El-Sayed M El-Rabaie, and Fathi E Abd El-Samie · 2023
Closest in time.
Diffbir: Towards blind image restoration with generative diffusion prior
Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Ben Fei, Bo Dai, Wanli Ouyang, Yu Qiao, and Chao Dong · 2023
Closest in time.
Dwa: Differential wavelet amplifier for image super-resolution
Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, and Andreas Dengel · 2023
Closest in time.
Hitchhiker’s guide to super-resolution: Introduction and recent advances
Brian B. Moser, Federico Raue, Stanislav Frolov, Sebastian Palacio, Jörn Hees, and Andreas Dengel · 2023
Closest in time.
Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
Closest in time.
Exploiting diffusion prior for real-world image super-resolution
Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy · 2023
Closest in time.
Partdiff: Image super-resolution with partial diffusion models
Kai Zhao, Alex Ling Yu Hung, Kaifeng Pang, Haoxin Zheng, and Kyunghyun Sung · 2023
Closest in time.
Cold diffusion: Inverting arbitrary image transforms without noise
Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2024
Closest in time.
Towards effective multiple-in-one image restoration: A sequential and prompt learning strategy
Xiangtao Kong, Chao Dong, and Lei Zhang · 2024
Closest in time.
Skipdiff: Adaptive skip diffusion model for high-fidelity perceptual image super-resolution
Xiaotong Luo, Yuan Xie, Yanyun Qu, and Yun Fu · 2024
Closest in time.
A study in dataset pruning for image super-resolution
Brian B Moser, Federico Raue, and Andreas Dengel · 2024
Closest in time.
Diffusion models, image super-resolution and everything: A survey
Brian B Moser, Arundhati S Shanbhag, Federico Raue, Stanislav Frolov, Sebastian Palacio, and Andreas Dengel · 2024
Closest in time.
Lazy diffusion transformer for interactive image editing, 2024
Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, and Michaël Gharbi · 2024
Closest in time.