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arXiv · 2102.06155

Learning local regularization for variational image restoration

Jean Prost·Antoine Houdard·Andrés Almansa·Nicolas Papadakis
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Abstract

In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. This regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. The regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. This yields a regularization function that can be incorporated in any image restoration problem. The efficiency of the framework is finally shown on denoising and deblurring applications.

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Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis. 2021-02-11. Learning local regularization for variational image restoration. https://arxiv.org/abs/2102.06155

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