  Plug-and-play denoisers can be used to perform generic image restoration
tasks independent of the degradation type. These methods build on the fact that
the Maximum a Posteriori (MAP) optimization can be solved using smaller
sub-problems, including a MAP denoising optimization. We present the first
end-to-end approach to MAP estimation for image denoising using deep neural
networks. We show that our method is guaranteed to minimize the MAP denoising
objective, which is then used in an optimization algorithm for generic image
restoration. We provide theoretical analysis of our approach and show the
quantitative performance of our method in several experiments. Our experimental
results show that the proposed method can achieve 70x faster performance
compared to the state-of-the-art, while maintaining the theoretical perspective
of MAP.
