  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.
