  Variational method and deep learning method are two mainstream powerful
approaches to solve inverse problems in computer vision. To take advantages of
advanced optimization algorithms and powerful representation ability of deep
neural networks, we propose a novel deep network for image reconstruction. The
architecture of this network is inspired by our proposed accelerated extra
proximal gradient algorithm. It is able to incorporate non-local operation to
exploit the non-local self-similarity of the images and to learn the nonlinear
transform, under which the solution is sparse. All the parameters in our
network are learned from minimizing a loss function. Our experimental results
show that our network outperforms several state-of-the-art deep networks with
almost the same number of learnable parameter.
