Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

中文翻译：

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基于图像处理的显微镜自动调焦方法

显微镜视觉分析应用在许多领域。传统的方式是用人眼观察，手动对焦，获得被观察物体的图像。但是，随着观察对象越来越精细，显微镜的放大倍数也要求越来越大。手动对焦的方法不能保证显微镜在使用中的最佳对焦位置。因此，在本文中，我们正在研究现有的自动对焦技术和显微镜基于图像处理的自动对焦方法，它有别于传统的手动对焦方法。基于图像处理的显微镜自动对焦方法不需要目标位置、光学系统焦距等信息，直接对采集到的图像进行对焦。首先，针对传统基于小波的图像锐度评价算法计算量大、实时性差的问题，提出一种改进的基于小波的图像锐度评价算法；其次，针对传统聚焦窗口选择方法选择的窗口是固定的情况，本文采用自适应聚焦窗口选择方法增加聚焦窗口。最后，本文研究了极值搜索策略。为了避免聚焦曲线中局部极值的干扰，本文提出了一种改进的爬山算法来实现聚焦搜索的准确性。仿真结果表明，改进的小波变换图像清晰度评价算法能够提高清晰度评价性能，改进后的爬山算法可以减少局部极值的影响，提高搜索算法的精度。综上所述，可以得出结论，本文提出的基于图像处理的方法具有良好的调焦效果，能够满足显微镜自动调焦抗干扰和极值搜索的需要。