This paper proposes a generalized accelerated prox- imal gradient (GAPG) approach for solving total variation (TV) based image restoration problems.
We have presented a framework of learning PDEs from training data for image restoration.
We propose a new criterion for designing the regularizing filter to solve an ill-posed problem and this criterion reveals the implicit assumption made by regularizing filters.