Restoration of Spatially-Varying Motion-Blurred Images

Restoration of spatially-varying blurred images is extensively required for various computer systems. In this paper, we present a new spatially-varying blur detection and restoration method. Motion blur is detected automatically from an individual image. Initially, the blurring kernel length and direction are estimated by finding the kernel that maximizes the likelihood of a blurred local window. This is achieved by incorporating either vertical or positive diagonal kernels with various lengths. Then, initial blur regions are estimated using a kernel specific feature. Next, the initial blur regions are refined with the support of the image segmentation (CCP) method and neighboring information. Finally, Blurred regions are recovered using the best-estimated kernel. Comparisons with the most successful methods reported in the literature demonstrate performance improvements in blur detection and restoration results.