Department of Automation, Tsinghua University, 100084, Beijing, China.
Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
Nat Commun. 2023 Jul 11;14(1):4118. doi: 10.1038/s41467-023-39860-0.
The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.
光学显微镜通常是一种体积大、价格昂贵但性能有限的仪器。在这里，我们报告了一种集成显微镜，它实现了超越商业显微镜（5×，NA 0.1 物镜）的光学性能，但仅在 0.15cm 和 0.5g 处，其尺寸比传统显微镜小五个数量级。为了实现这一点，提出了一个渐进式优化管道，与端到端优化相比，该管道系统地优化了非球面透镜和衍射光学元件，内存减少了 30 多倍。通过设计用于光学设计中空间变化反卷积的仿真监督深度神经网络，与传统显微镜相比，我们的方法在景深方面提高了 10 多倍，并且在各种样本中有很好的泛化能力。为了展示独特的优势，集成显微镜被安装在没有任何附件的手机中，用于便携式诊断应用。我们相信，我们的方法通过集成非球面光学、计算光学和深度学习，为设计小型化高性能成像系统提供了一个新的框架。
