纳米颗粒成像过程中，离焦位置的粒子团簇和大颗粒杂质产生明亮的弥散斑，导致现有的对焦算法无法实现自动对焦功能。利用基于大津算法的二值化分割和形态学开闭方法，使离散的弥散斑聚合为一个区域，并利用连通域标记方法筛选剔除大面积的光斑区域，构造四邻域水平-对角平方函数和阈值四邻域水平-对角开方函数，将函数分别作为粗对焦和精对焦的评价指标，提高了自动对焦搜索的准确性和可靠性。实拍离焦序列图，并与5种常用的评价算法进行对比，结果表明所提自动对焦评价算法具有良好的鲁棒性、无偏性和单峰性。

In nanoparticle imaging, particle clusters and large impurity particles in the defocused position cause bright spots, thus hindering the existing focusing algorithms in realizing the autofocus function. This study used binarization segmentation based on the Otsu algorithm, as well as morphological opening and closing methods, to aggregate the dispersed diffuse spots into one area. Furthermore, the connected domain labeling method was used to filter out large regions of the spot area. A four-neighborhood level-diagonal square function and threshold-four-neighborhood level-diagonal square root function were constructed and used as the evaluation indicators for the coarse and fine focus, respectively, thereby improving the accuracy and reliability of autofocus search. The defocus sequence diagram was obtained and the proposed algorithm was compared to the five commonly used evaluation algorithms. The results demonstrate that the proposed autofocus evaluation algorithm is highly robust, unbiased, and unimodal.