Image clarity evaluation algorithm based on region weighting

Home > Archive>Volume 47, Issue 11, 2024 >44-50

Image clarity evaluation algorithm based on region weighting
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                        Zheng BowenZheng Bowen
1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China； 2.University of Chinese Academy of Sciences，Beijing 100049,China
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Liu ShaojinLiu Shaojin
1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China； 2.University of Chinese Academy of Sciences，Beijing 100049,China
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Shen ChengwuShen Chengwu
1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China； 2.University of Chinese Academy of Sciences，Beijing 100049,China
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Xie ZhongxuXie Zhongxu
1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China； 2.University of Chinese Academy of Sciences，Beijing 100049,China
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Liu XuLiu Xu
1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China； 2.University of Chinese Academy of Sciences，Beijing 100049,China
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Affiliation:1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences，Changchun 130033,China; 2.University of Chinese Academy of Sciences，Beijing 100049,China
Clc Number:TP751.1; TN911.73
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Abstract:

Aiming at the problems of low sensitivity and many local extreme points of the clarity evaluation function during the focusing process of the photoelectric imaging system. This paper proposes an image clarity evaluation algorithm based on regional weighting. First, the algorithm adopts a threshold based on the traditional Laplacian evaluation function to improve the anti-noise and the ratio of clarity. Then, the algorithm also uses the image gradient map to calculate the regional clarity weighting factor which can optimize the variance of flat part of focusing curve. Experimental results show that compared with the most traditional clarity evaluation functions, the clarity ratio of this algorithm is increased by about 2.7 times、the sensitivity increases by about 1.9 times and the variance of flat part of focusing curve can be reduced to 1/6 of the traditional Laplacian evaluation function.In general, this algorithm has the advantages of high clarity ratio and sensitivity、low variance of flat part of focusing curve and has better evaluation performance when the image content is complex.

Key words:imaging systems;clarity evaluation;auto-focus;regional weighting;image gradient

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Online: October 12,2024
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