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Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data.
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Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2014
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Diederik P Kingma and Jimmy Ba · 2014
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Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown · 2018
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Deblurgan: Blind motion deblurring using conditional adversarial networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas · 2018
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Noise2noise: Learning image restoration without clean data
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“zero-shot” super-resolution using deep internal learning
Assaf Shocher, Nadav Cohen, and Michal Irani · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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Esrgan: Enhanced super-resolution generative adversarial networks
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Visual prompting via image inpainting
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Classifier-free diffusion guidance
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Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
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Uformer: A general u-shaped transformer for image restoration
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Restormer: Efficient transformer for high-resolution image restoration
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Masked image training for generalizable deep image denoising
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srgb real noise synthesizing with neighboring correlation-aware noise model
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Blind image deconvolution using variational deep image prior
Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol, and Yee-Hong Yang · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
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Resshift: Efficient diffusion model for image super-resolution by residual shifting
Zongsheng Yue, Jianyi Wang, and Chen Change Loy · 2023
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Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution
Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, and Wenming Yang · 2023
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Unsupervised blind image deblurring based on self-enhancement
Lufei Chen, Xiangpeng Tian, Shuhua Xiong, Yinjie Lei, and Chao Ren · 2024
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Diffusion-based adaptation for classification of unknown degraded images
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Test-time degradation adaptation for open-set image restoration
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srgb real noise modeling via noise-aware sampling with normalizing flows
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Deep variational network toward blind image restoration
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