Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold  

作　　者：Usman Ali Muhammad Tariq Mahmood 

机构地区：[1]Future Convergence Engineering,School of Computer Science and Engineering,Korea University of Technology and Education,1600,Chungjeol-ro,Byeongcheon-myeon,Cheonan,31253,Korea

出　　处：《Computers, Materials & Continua》2022年第4期1597-1611,共15页计算机、材料和连续体（英文）

基　　金：This work is supported by the BK-21 FOUR program and by the Creative Challenge Research Program(2021R1I1A1A01052521)through National Research Foundation of Korea(NRF)under Ministry of Education,Korea.

摘　　要：Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

关 键 词：Adaptive threshold blur measure defocus blur segmentation local binary pattern support vector machine 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]