摘要
针对显微镜自动聚焦时传统的图像清晰度评价算法容易受外界因素的干扰进而影响自动聚焦的精度和速度的问题,在对常用的基于图像梯度的清晰度评价算法及其他算法的研究基础上,提出了一种结合了Variance函数与Brenner函数的优点新的图像清晰度评价算法,建立其数学模型,并与传统的Brenner函数、Tenengrad函数等进行仿真对比。分析了噪声的影响,验证了高斯及中值滤波去除噪声的效果。仿真结果表明提出的图像清晰度评价算法计算量小,鲁棒性强,精度高。
Aiming at the problem that the traditional image definition evaluation algorithm is easy to be interfered by external factors,which will affect the precision and speed of automatic focusing,based on the research of commonly used image gradient based sharpness evaluation algorithm and other algorithms,a new image sharpness evaluation algorithm combining the advantages of variance function and Brenner function is proposed The mathematical model is established and compared with the traditional Brenner function and tenengrad function. The effect of noise is analyzed,and the effect of Gaussian and median filtering is verified. The simulation results show that the proposed algorithm has the advantages of low computation,strong robustness and high precision.
作者
王灿芳
崔良玉
阎兵
WANG Can-fang;CUI Liang-yu;YAN Bing(Tianjin Vocational and Technical Normal University,Tianjin 300222,China;Tianjin Key Laboratory of High-speed Cutting and Precision Machining,Tianjin 300222,China)
出处
《装备制造技术》
2020年第10期78-82,共5页
Equipment Manufacturing Technology
关键词
显微图像
自动聚焦
图像处理
图像清晰度评价算法
microscopic image
automatic focusing
image processing
image sharpness evaluation algorithm
