  A fast and effective motion deblurring method has great application values in
real life. This work presents an innovative approach in which a self-paced
learning is combined with GAN to deblur image. First, We explain that a proper
generator can be used as deep priors and point out that the solution for
pixel-based loss is not same with the one for perception-based loss. By using
these ideas as starting points, a Bi-Skip network is proposed to improve the
generating ability and a bi-level loss is adopted to solve the problem that
common conditions are non-identical. Second, considering that the complex
motion blur will perturb the network in the training process, a self-paced
mechanism is adopted to enhance the robustness of the network. Through
extensive evaluations on both qualitative and quantitative criteria, it is
demonstrated that our approach has a competitive advantage over
state-of-the-art methods.
