Related papers: Equivariant Denoisers for Image Restoration

Equivariant Denoisers for Image Restoration

URL: http://arxiv.org/abs/2412.05343v1
Date: Fri, 06 Dec 2024 10:22:00 GMT
Title: Equivariant Denoisers for Image Restoration
Authors: Marien Renaud, Arthur Leclaire, Nicolas Papadakis,
Abstract summary: We propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and optimization. We analyze the convergence of this algorithm and discuss its practical benefit.
Score: 8.865896660863681
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Abstract: One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and stochastic optimization. We analyze the convergence of this algorithm and discuss its practical benefit.

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