Patch selection for single image deblurring based on a coalitional game

Abstract

Most single-image deblurring methods estimate the blur kernel using whole image, however, that may lead to incorrect estimation and more computations. In this paper, we focus on accelerating the blind deconvolution algorithm and increasing the accuracy of kernel estimation by using only a small region in image to perform the process of kernel estimation. Then, the problem now is to find the most proper region. At first, we found informative pixels to locate useful patches. Inspiring by game theory, we propose a coalitional game based patch selection method to choose a group of patches for kernel estimation. In this game, each patch represents a player, and our purpose is to find a coalition that has the maximal payoff. Shapley Value is applied to fairly distribute the utility to each player. We show the speed-up and the quality improvement of our method both on real-world and synthetic images.