Improved Patch-Based Learning for Image Deblurring

Most recent image deblurring methods only use valid information found in input image as the clue to fill the deblurring region. These methods usually have the defects of insufficient prior information and relatively poor adaptiveness. Patch-based method not only uses the valid information of the input image itself, but also utilizes the prior information of the sample images to improve the adaptiveness. However the cost function of this method is quite time-consuming and the method may also produce ringing artifacts. In this paper, we propose an improved non-blind deblurring algorithm based on learning patch likelihoods. On one hand, we consider the effect of the Gaussian mixture model with different weights and normalize the weight values, which can optimize the cost function and reduce running time. On the other hand, a post processing method is proposed to solve the ringing artifacts produced by traditional patch-based method. Extensive experiments are performed. Experimental results verify that our method can effectively reduce the execution time, suppress the ringing artifacts effectively, and keep the quality of deblurred image.