基于区域加权的图像清晰度评价算法
Image clarity evaluation algorithm based on region weighting
郑博文 1刘绍锦 1沈铖武 1谢忠旭 1刘旭1
作者信息
- 1. 中国科学院长春光学精密机械与物理研究所 长春 130033;中国科学院大学 北京 100049
- 折叠
摘要
针对光电成像系统对焦过程中清晰度评价函数的灵敏度低、局部极值点较多的问题,在对传统清晰度评价函数研究的基础上,提出一种基于区域加权的图像清晰度评价算法.此算法首先在传统Laplacian评价函数基础上引入了阈值,提升算法的抗噪性及清晰度比率,其次利用图像梯度图计算区域清晰度加权因子,优化算法的平缓区波动量.实验结果表明,与大多数传统清晰度评价函数相比,该算法清晰度比率提升约2.7倍,灵敏度提升约1.9倍,平缓区波动量可以减少为传统Laplacian评价函数的1/6,并且在图像内容复杂时的评价性能更加可靠,具有清晰度比率与灵敏度高、局部极值点少的优点.
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.
关键词
成像系统/清晰度评价/自动对焦/区域加权/图像梯度Key words
imaging systems/clarity evaluation/auto-focus/regional weighting/image gradient引用本文复制引用
基金项目
吉林省科技发展计划项目(20220201096gx)
出版年
2024
NETL
NSTL
万方数据