Learning local regularization for variational image restoration
Idioma
en
Communication dans un congrès
Este ítem está publicado en
International Conference on Scale Space and Variational Methods in Computer Vision (SSVM'21), 2021-05-17, Cabourg. 2021-04-30, vol. LNCS 12679, p. 358-370
Springer
Resumen en inglés
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 ...Leer más >
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.< Leer menos
Palabras clave en inglés
image inverse problem
denoising
deblurring
Proyecto ANR
Repenser la post-production d'archives avec des méthodes à patch, variationnelles et par apprentissage - ANR-19-CE23-0027
Orígen
Importado de HalCentros de investigación