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Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters.
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Edward T Reehorst and Philip Schniter · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
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Denoising diffusion probabilistic models
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Maskgan: Towards diverse and interactive facial image manipulation
Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo · 2020
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Deep learning techniques for inverse problems in imaging
Gregory Ongie, Ajil Jalal, Christopher A Metzler, Richard G Baraniuk, Alexandros G Dimakis, and Rebecca Willett · 2020
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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I-haze: a dehazing benchmark with real hazy and haze-free indoor images
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O-haze: a dehazing benchmark with real hazy and haze-free outdoor images
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Stochastic solutions for linear inverse problems using the prior implicit in a denoiser
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Learning to restore hazy video: A new real-world dataset and a new method
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A survey on deep semi-supervised learning
Xiangli Yang, Zixing Song, Irwin King, and Zenglin Xu · 2022
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Sud: Supervision by denoising for medical image segmentation
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael Thompson Mccann, Marc Louis Klasky, and Jong Chul Ye · 2023
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