Abstract
Recovering an un-blurred image from a single, motion-blurred photograph has long been a fundamental research problem in digital imaging. Recently, most deblurring algorithm use whole image as input to estimate the blur kernel based on a coarse-to-fine MAP approach. However, not all pixels of the input blurred image are informative. Many regions in the images, smooth regions for example, do not contribute much for estimating the blur kernel, even take more time to compute. Thus, we present the regions selection method based on strong edges to automatically select the good regions for estimating the blur kernel. At the beginning, we analyze the blurred images and the edges correspond with its clear images, retraining the structured forest based on [1]. The trained module takes the blurred image as input and find the candidate regions which contain strong edges. Then we use the spatial information to decide whether to merge these regions or not. After that, we can get the patch which can contribute much for estimating the blur kernel. At the estimation time, we find that too much detail in the latent image may affect the correctness of the kernel. Consequently, we apply L0 Gradient Minimization on the latent image before each iteration to remove extra details and preserve main structure simultaneously. Then extract the edges from the structure to improve the quality of kernel estimation.