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In this paper, we introduce Plug-and-Play (PnP) Flow Matching, an algorithm for solving imaging inverse problems.
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Plug-and-play methods provably converge with properly trained denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2019
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Energy dissipation with plug-and-play priors
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Yu Sun, Brendt Wohlberg, and Ulugbek S Kamilov · 2019
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Multisample flow matching: Straightening flows with minibatch couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky T. Q. Chen · 2023
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DeepInverse: A deep learning framework for inverse problems in imaging, 2023
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Zero-shot image restoration using denoising diffusion null-space model
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Denoising diffusion models for plug-and-play image restoration
Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, and Luc Van Gool · 2023
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D-Flow: Differentiating through flows for controlled generation
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Convolutional proximal neural networks and plug-and-play algorithms
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Score-based generative modeling through stochastic differential equations
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Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, and Yaron Lipman · 2024
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