基于全自动控制显微镜的自动聚焦算法研究

图像自动聚焦评价函数的选择是垒自动控制显微镜无源方式自动聚焦系统的关键问露。对几种主要的图像聚焦评价函散(灰度方差算子、灰度梯度算子、能量谱方法等)进行了比较、研究，并在此基础上首次将改进的Laplacian算子作为聚焦评价函数引人自动聚焦之中,同时为了消除噪声的影响，引入了步长和阈值两个参数。实验结果表明，改进的LapIacian算子比其他评价函数更为准确、稳定和可靠，该算法已成功应用于显微镜自动词焦系统中。

Study on Auto Focusing Algorithm for Automatic Microscope

Choosing auto focusing evaluation function for images is a key factor for passive auto focusing system of automatic microscope. The basic requirements for a practical auto focusing system are speed, sharpness and robustness to noise. With the relationship between focused and defocused images of a scene, some well known focusing measures (such as gradation variance operator, gradation gradient operator and energy spectrum measure) have been investigated. Based on them, a sum modified Laplacian (SML) operator has been proposed as focusing measures for the first time. The operator is applied to measure the relative sharpness of image sequence at different object distances. Step and threshold are introduced to effectively alleviate the effect of the noise. All of the above mathematical models have been analyzed and compared. Experimental results are presented that demonstrate the accuracy and robustness of the proposed method. The results show that the SML operator is more accurate, stable and reliable than other auto focusing evaluation functions for microscopy images. The algorithm has been applied successfully to automatic focusing system of microscope and testified to be feasible and effective.