Blind Image Deblurring via Minimizing Similarity Between Fuzzy Sets on Image Pixels

Most existing image deblurring methods construct statistical prior to describe the difference between blur and clear image. They discard the position information and ignore pixel feature changing in deblurring, which results in inferior restoration performance for images unsatisfying corresponding assumptions. Intuitively, fuzziness of pixel belonging to different image regions will reduce along with image deblurring. This phenomenon could intrinsically describe the pixel characteristic. To this end, we analyze fuzziness of pixels and objects in a blurry image, and utilize the similarity between two fuzzy objects on image pixels to depict the blur degree of an image, which is inspired by overlap functions and overlap indices. To minimize the similarity between fuzzy objects, we introduce the non-parameters model to construct an integer programming problem. Energy minimization could significantly reduce the similarity between two fuzzy objects. Experimental results show that the proposed method can achieve better performance than the state-of-the-art blind deblurring methods on benchmark datasets and natural images.