Single-Shot Plug-and-Play Methods for Inverse Problems

• URL: http://arxiv.org/abs/2311.13682v2
• Date: Mon, 11 Nov 2024 15:31:02 GMT
• Title: Single-Shot Plug-and-Play Methods for Inverse Problems
• Authors: Yanqi Cheng, Lipei Zhang, Zhenda Shen, Shujun Wang, Lequan Yu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero,
• Abstract summary: Plug-and-Play priors in inverse problems have become increasingly prominent in recent years. Existing models predominantly rely on pre-trained denoisers using large datasets. In this work, we introduce Single-Shot perturbative methods, shifting the focus to solving inverse problems with minimal data.
• Score: 24.48841512811108
• License:
• Abstract: The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly prominent in recent years. This preference is based on the mathematical equivalence between the general proximal operator and the regularised denoiser, facilitating the adaptation of various off-the-shelf denoiser priors to a wide range of inverse problems. However, existing PnP models predominantly rely on pre-trained denoisers using large datasets. In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data. First, we integrate Single-Shot proximal denoisers into iterative methods, enabling training with single instances. Second, we propose implicit neural priors based on a novel function that preserves relevant frequencies to capture fine details while avoiding the issue of vanishing gradients. We demonstrate, through extensive numerical and visual experiments, that our method leads to better approximations.

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