Abstract
Most of single image blind deblurring algorithms use the whole image to search for the maximum a posteriori (MAP) solution to estimate the blur kernel for the blurred image based on the coarse-to-fine iterations. We observed that using the whole blurred image to estimate its blur kernel usually needs a lot of complicated calculation. The quality of the latent image obtained from this method is not always good because the small gradients of the image may lead to a lot of errors in the kernel estimation. We propose an algorithm based on image decomposition and patch selection to select the helpful patch for the kernel estimation to avoid the problems given above. First, we use the gradient magnitudes of the blurry photo and the distribution of gradients within a natural image to decompose the blurry image into informative structure and insignificant smooth region. Second, we find the most informative pixels from the structure region. If the image contains saturation regions, we will also find the useful pixels within these regions. These pixels will form different sizes of patches with their nearest neighbor. We use the size of patch and the values of informative pixels within this patch to calculate the utility of each patch. Finally, we select the patch with the maximum utility as the initial patch for the kernel estimation. Our experimental results show that our algorithm can save time for the deblurring while preserving the good qualities of deblurred photos both on real-world and synthetic images.