  In this paper, we propose a fully convolutional networks for iterative
non-blind deconvolution We decompose the non-blind deconvolution problem into
image denoising and image deconvolution. We train a FCNN to remove noises in
the gradient domain and use the learned gradients to guide the image
deconvolution step. In contrast to the existing deep neural network based
methods, we iteratively deconvolve the blurred images in a multi-stage
framework. The proposed method is able to learn an adaptive image prior, which
keeps both local (details) and global (structures) information. Both
quantitative and qualitative evaluations on benchmark datasets demonstrate that
the proposed method performs favorably against state-of-the-art algorithms in
terms of quality and speed.
