Zhang Jing, Fu Yong-Feng, Shen Hao, Liu Quan, Sun Li-Ning, Chen Li-Guo
School of Mechanical and Electrical Engineering, Soochow University, No.8 Jixue Road, Suzhou City, Jiangsu, 215000, China.
School of Computer Science and Technology, Soochow University, No.333 Ganjiang East Road, Suzhou City, Jiangsu, 215006, China.
Microsyst Nanoeng. 2024 Dec 24;10(1):201. doi: 10.1038/s41378-024-00845-8.
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.
显微成像在科学研究、生物医学研究和工程应用中是一种关键工具，迫切需要系统小型化以及快速、精确的自动对焦技术。然而，传统显微镜和自动对焦方法在实现这一目标时面临硬件限制和软件速度慢的问题。作为回应，本文提出了一种利用基于深度强化学习的自动对焦（DRLAF）实现的自适应液体透镜显微镜系统。所提出的研究采用了具有快速变焦响应的定制液体透镜，将其视为一个“智能体”。原始图像被用作“状态”，电压调整代表“动作”。采用深度强化学习直接从捕获的图像中学习对焦策略，实现端到端自动对焦。与仅依赖清晰度评估作为模型标签或输入的方法不同，我们的方法涉及开发一种有针对性的奖励函数，这已被证明能显著提高显微镜自动对焦任务的性能。我们探索了各种动作组设计方法，并将显微镜自动对焦速度提高到平均3.15个时间步长。此外，还提出了具有随机采样训练的并行“状态”数据集列表，这增强了模型对未知样本的适应性，从而提高了其泛化能力。实验结果表明，所提出的带有DRLAF的液体透镜显微镜具有很高的鲁棒性，与传统搜索算法相比速度提高了79%，成功率为97.2%，并且与其他深度学习方法相比具有更强的泛化能力。
