Iterative Blind Image Motion Deblurring via Learning a No-Reference Image Quality Measure

In this paper, we propose a learning-based image restoration algorithm for restoring images degraded by uniform motion blurs. The motion blur parameters are first approximately estimated from the robust global motion estimation result. Then, we present a novel framework to refine the image restoration iteratively based on recursively adjusting the motion blur parameters for image restoration to achieve the best image quality measure. Note that a no-reference image quality assessment model is learned by training a RBF neural network from a collection of representative training images simulated with different motion blurs. Experimental results blurred on real videos are given to demonstrate the performance of the proposed blind motion deblurring algorithm.