LMEPOP and Fuzzy Logic Based Intelligent Technique for Segmentation of Defocus Blur

Abstract

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this project, we propose a sharpness metric based on the the Local maximum edge position octal pattern and a robust segmentation algorithm to separate in- and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multiscale fuzzy inference, this work obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and has high speed advantage over others.