Autofocus Evaluation Algorithm for Nanoparticle Imaging
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.
汪路涵, 巩岩, 张艳微, 高若谦, 郎松, 曹选. 纳米颗粒成像自动对焦评价算法[J]. 激光与光电子学进展, 2023, 60(4): 0410021. Luhan Wang, Yan Gong, Yanwei Zhang, Ruoqian Gao, Song Lang, Xuan Cao. Autofocus Evaluation Algorithm for Nanoparticle Imaging[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0410021.