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Learning Local Regularization for Variational Image Restoration

2021; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-75549-2_29

1611-3349

Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis,

Advanced Image Fusion Techniques

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.