Visual sensitivity filtering based local maximum variation prior for blind image deblurring

Abstract

Blind image deblurring aims to recover the latent sharp image from a given blurred image when the blur kernel is unknown. To solve this ill-posed problem, many researchers have studied various priors to guide the deblurring process. However, whether the image is sharp or not is mostly based on the subjective judgment of human eyes, and there is no unified objective standard. Since the response of blur to visual sensitivity can be modeled by a filtering method, we use human visual system (HVS) convolution filter to filter the target image to simulate the corresponding visual sensitivity. At the same time, we find that the local maximum variation values of the filtered image will be reduced after blurring process. Therefore, we propose a local maximum variation prior (LMV) based on visual sensitivity filtering and construct a new energy function according to LMV. Our method performs well in various scenarios. Compared with the most advanced algorithms, our model achieves good results on both benchmark datasets and natural images.