Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning  

作　　者：Jing Zhang Yong-feng Fu Hao Shen Quan Liu Li-ning Sun Li-guo Chen 

机构地区：[1]School of Mechanical and Electrical Engineering,Soochow University,No.8 Jixue Road,Suzhou City,Jiangsu 215000,China [2]School of Computer Science and Technology,Soochow University,No.333 Ganjiang East Road,Suzhou City,Jiangsu 215006,China

出　　处：《Microsystems & Nanoengineering》2024年第6期559-571,共13页微系统与纳米工程(英文)

基　　金：supported by the National Key R&D Program of China(2022YFB3207100).

摘　　要：Microscopic imaging is a critical tool in scientific research,biomedical studies,and engineering applications,with an urgent need for system miniaturization and rapid,precision autofocus techniques.However,traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this goal.In response,this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus(DRLAF).The proposed study employs a custom-made liquid lens with a rapid zoom response,which is treated as an“agent.”Raw images are utilized as the“state”,with voltage adjustments representing the“actions.”Deep reinforcement learning is employed to learn the focusing strategy directly from captured images,achieving end-to-end autofocus.In contrast to methodologies that rely exclusively on sharpness assessment as a model’s labels or inputs,our approach involved the development of a targeted reward function,which has proven to markedly enhance the performance in microscope autofocus tasks.We explored various action group design methods and improved the microscope autofocus speed to an average of 3.15 time steps.Additionally,parallel“state”dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples,thereby improving its generalization capability.The experimental results demonstrate that the proposed liquid lens microscope with DRLAF exhibits high robustness,achieving a 79%increase in speed compared to traditional search algorithms,a 97.2%success rate,and enhanced generalization compared to other deep learning methods.

关 键 词：DEEP HARDWARE GENERALIZATION 

分 类 号：TN723[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]