Abstract
In this thesis, we propose a learning-based image restoration algorithm for restoring images degraded by linear 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. The temporal information from the frame sequence is also integrated by temporally filtering the refined motion blur parameters for the whole sequence. Finally, we apply some post-processing skills, including the histogram equalization and the bilateral filtering, to improve the image deblurring results. Note that a no-reference image quality assessment model is learned by training Support Vector Regression (SVR) from a collection of representative training images simulated by degradation and restoration with different combinations of motion blur parameter values. Some experimental results on the deblurring of simulated blurred images and real videos are given to demonstrate the performance of the proposed blind motion deblurring algorithm.