Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

作     者：Muhammad Tariq Mahmood 

作者机构：Future Convergence EngineeringSchool of Computer Science and EngineeringKorea University of Technology and EducationCheonan31253Byeongcheon-myeonKorea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第3期

页      面：4867-4882页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the BK-21 FOUR program through National Research Foundation of Korea(NRF)under Ministry of Education 

主　　题：Blur measure blur segmentation sharpness measure genetic programming support vector machine 

摘      要：Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of *** this paper,we propose an effective method for blur detection and segmentation based on transfer learning *** proposed method consists of two separate *** the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the *** GP model method uses the multiresolution features of the image and it provides an improved blur *** the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur *** performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art *** comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.