  Blind motion deblurring is one of the most basic and challenging problems in
image processing and computer vision. It aims to recover a sharp image from its
blurred version knowing nothing about the blur process. Many existing methods
use Maximum A Posteriori (MAP) or Expectation Maximization (EM) frameworks to
deal with this kind of problems, but they cannot handle well the figh frequency
features of natural images. Most recently, deep neural networks have been
emerging as a powerful tool for image deblurring. In this paper, we prove that
encoder-decoder architecture gives better results for image deblurring tasks.
In addition, we propose a novel end-to-end learning model which refines
generative adversarial network by many novel training strategies so as to
tackle the problem of deblurring. Experimental results show that our model can
capture high frequency features well, and the results on benchmark dataset show
that proposed model achieves the competitive performance.
