Research of Image Sharpness Assessment Algorithm for Autofocus

doi:10.1109/ICIVC47709.2019.8980980

OPT OpenIR > 光电跟踪与测量技术研究室

	Research of Image Sharpness Assessment Algorithm for Autofocus
	Her, Lilin 1,2; Yang, Xiaojun1
	2019-07
会议名称	4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019
会议录名称	2019 IEEE 4th International Conference on Image, Vision and Computing, ICIVC 2019
页码	93-98
会议日期	2019-07-05
会议地点	Xiamen, China
出版者	Institute of Electrical and Electronics Engineers Inc.
产权排序	1
摘要	With the wide application of imaging system in security monitoring, aerospace, medical image and other fields, how to capture the clear image of the target in real time and automatically is particularly important. Image sharpness evaluation function is the key to evaluate the imaging quality of various imaging systems. The spatial gradient evaluation algorithm is based on the direct processing of image pixels, and the calculation is simple and intuitive. In this paper, we compare the performance of image sharpness evaluation functions in four spatial domains through two sets of atlases with different background richness. Experiments show that Benner algorithm has high scene adaptability and strong anti-jamming ability; Laplace algorithm has high sensitivity and can get results quickly in different size images; Tenengrad algorithm can reduce the occurrence of local extremum after selecting a certain threshold; Robert algorithm has poor unimodality and accuracy in two sets of atlas test. © 2019 IEEE.
关键词	sharpness evaluation function autofocus gradient operator brenner algorithm
作者部门	光电跟踪与测量技术研究室
DOI	10.1109/ICIVC47709.2019.8980980
收录类别	EI
ISBN号	9781728123257
语种	英语
EI入藏号	20201908641571
引用统计
文献类型	会议论文
条目标识符	http://ir.opt.ac.cn/handle/181661/93433
专题	光电跟踪与测量技术研究室
作者单位	1.Chinese Academy of Sciences, Xi'An Institute of Optics and Precision Mechanics, Xi'an, China; 2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Her, Lilin,Yang, Xiaojun. Research of Image Sharpness Assessment Algorithm for Autofocus[C]:Institute of Electrical and Electronics Engineers Inc.,2019:93-98.

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