Image deblurring with blur kernel estimation from a reference image patch

Po-Hao Huang, Yu-Mo Lin, Shang-Hong Lai

Abstract: In this paper, we propose a new approach for image deblurring from two images, non-blurred and blurred, in different poses by exploiting the co-existing planar object in both views. We focus on the problem of aligning the corresponding image patches, which are the co-existing planar object, in both images and propose an iterative two-stage algorithm for patch alignment and kernel estimation. In the first stage, we extend the intensity-based alignment method to find the geometric transformation between patches, and then the aligned image patches are used for blur kernel estimation in the second stage. These two stages are repeated until convergence. Furthermore, the proposed algorithm can also be used when the geometric relationship between the two images is a homography or an approximate homography, such as images from image mosaic. Experimental results on real images are given to demonstrate its performance.