Segmentation Through Lbp Based Defocus Blur

Abstract

When an image is captured by any optical imaging devices in that image, defocus blurs is the common undesirable issue. So defocus blur is extremely common in images captured using optical imaging systems. The presence of the blur is one of the major drawbacks which occur frequently in the processing of the digital images in the real time scenario. We proposed sharpness metric in this paper based on Local Binary Patterns (LBP) and a robust segmentation algorithm for the defocus blur. The proposed sharpness metric exploits the observation that the majority local image patches in blurred regions have considerably fewer of bounds native binary patterns compared with those in sharp regions. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and comparator methods. The results shows that our algorithm achieves comparative segmentation results with have big speed advantage over the others.