结合全局和局部灰度变化的显微图像自动聚焦函数

Auto-Focusing Function for Microscopic Images Based on Global and Local Gray-Scale Variation

显微图像自动聚焦的关键在于设计一个高灵敏度聚焦函数。由于显微图像细节多寡不确定,传统的梯度函数对细节较少的图像的灵敏度不够高。针对该问题,提出了一种结合全局和局部灰度变化的VarGrad显微图像自动聚焦函数。根据显微图像的特点,VarGrad函数利用聚焦窗口将基于全局灰度变化的灰度方差函数与基于局部灰度变化的梯度函数有机结合,无论图像细节是否丰富,都呈现较高的灵敏度。实验利用两组细节丰富程度不同的外周血细胞图像序列对VarGrad函数进行了定量评估。实验结果表明,与几种典型的聚焦函数相比,在图像细节较丰富和图像细节较少两种情况下,VarGrad函数在清晰度比率、陡峭度和清晰度变化率3种灵敏度指标上均提高了30%以上。

The key to auto-focusing of microscopic images is to design a high sensitivity focusing function. Due to uncertainty of the details among different microscopic images, traditional gradient functions are less sensitive to less detailed images. To solve the problem, an auto focusing function, VarGrad, combining global and local gray-scale variation, is proposed. According to the characteristics of microscopic images, VarGrad function comhines the gray variance function based on global gray-scale variation with the gray gradient function based on local gray-scale variation by using focusing windows. The VarGrad function exhibits high sensitivity regardless of image detail. We carry out quantitative evaluation of the proposed VarGrad function using two different peripheral blood cell image sequences with different image details. The experimental results show that three sensitivity indices, sharpness rate, steepness, and rate of change of sharpness, are improved by over 30% for detailed images and less detailed images, compared with that of several traditional typical focusing functions.