Learning local regularization for variational image restoration
This work addresses image restoration for computer vision applications, presenting an incremental improvement by learning a regularizer from unpaired data.
The authors tackled the problem of image restoration by learning a local regularization model using a fully convolutional neural network and Wasserstein GAN-based energy, resulting in a framework applicable to denoising and deblurring tasks.
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.