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Learning local regularization for variational image restoration

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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https://hal.archives-ouvertes.fr/hal-03139784
Contributor : Jean Prost <>
Submitted on : Friday, February 12, 2021 - 12:12:51 PM
Last modification on : Wednesday, February 17, 2021 - 3:31:01 AM

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  • HAL Id : hal-03139784, version 1
  • ARXIV : 2102.06155

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Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis. Learning local regularization for variational image restoration. 2021. ⟨hal-03139784⟩

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