Sun Ruiqing, Yang Delong, Hu Yao, Hao Qun, Li Xin, Zhang Shaohui
School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Changchun University of Science and Technology, Changchun 130022, China.
Biomed Opt Express. 2023 Jul 21;14(8):4205-4216. doi: 10.1364/BOE.495311. eCollection 2023 Aug 1.
Fourier Ptychographic Microscopy (FPM) is a computational technique that achieves a large space-bandwidth product imaging. It addresses the challenge of balancing a large field of view and high resolution by fusing information from multiple images taken with varying illumination angles. Nevertheless, conventional FPM framework always suffers from long acquisition time and a heavy computational burden. In this paper, we propose a novel physical neural network that generates an adaptive illumination mode by incorporating temporally-encoded illumination modes as a distinct layer, aiming to improve the acquisition and calculation efficiency. Both simulations and experiments have been conducted to validate the feasibility and effectiveness of the proposed method. It is worth mentioning that, unlike previous works that obtain the intensity of a multiplexed illumination by post-combination of each sequentially illuminated and obtained low-resolution images, our experimental data is captured directly by turning on multiple LEDs with a coded illumination pattern. Our method has exhibited state-of-the-art performance in terms of both detail fidelity and imaging velocity when assessed through a multitude of evaluative aspects.
傅里叶叠层显微镜（FPM）是一种实现大空间带宽积成像的计算技术。它通过融合从不同照明角度拍摄的多幅图像中的信息，解决了在大视场和高分辨率之间进行平衡的挑战。然而，传统的FPM框架总是存在采集时间长和计算负担重的问题。在本文中，我们提出了一种新颖的物理神经网络，通过将时间编码照明模式作为一个独特的层纳入其中来生成自适应照明模式，旨在提高采集和计算效率。我们进行了模拟和实验来验证所提方法的可行性和有效性。值得一提的是，与之前通过对每个顺序照明并获取的低分辨率图像进行后组合来获得复用照明强度的工作不同，我们的实验数据是通过以编码照明模式打开多个发光二极管直接捕获的。当通过多个评估方面进行评估时，我们的方法在细节保真度和成像速度方面都展现出了最先进的性能。
