Learning local regularization for variational image restoration

• URL: http://arxiv.org/abs/2102.06155v1
• Date: Thu, 11 Feb 2021 17:55:08 GMT
• Title: Learning local regularization for variational image restoration
• Authors: Jean Prost, Antoine Houdard, Andr\'es Almansa and Nicolas Papadakis
• Abstract summary: 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.
• Score: 3.5557219875516655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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