复合物理先验优化的Fourier重叠关联多参数神经网络
Fourier ptychography multi-parameter neural network with composite physical priori optimization
摘要：
傅立叶重叠关联显微术（FP）是近年来发展起来的一种用于显微超分辨成像的计算成像方法。通过依次开启LED阵列上不同位置的LED，获取包含不同空间频率分量的图像，可以在大视场的情况下实现高空间分辨率和定量相位成像。然而，FPM对系统结构和数据采集过程有很高的要求，如精确的LED位置、准确的聚焦和适当的曝光时间，这给其实际应用带来了很多限制。本文受人工神经网络的启发，提出了一种复合物理先验优化的傅立叶重叠关联多参数神经网络（FPMN）。提出了一种物理成像模型与数据驱动网络训练相结合的混合参数确定策略，用于恢复不同物理参数对应的网络多层，包括样本复变函数、系统光瞳函数、离焦距离、LED阵列位置偏差和照度波动等，其中，利用明暗场过渡低分辨率图像的特征来恢复LED阵列的位置偏差，而其他特征则在神经网络的训练过程中恢复。通过仿真和实际实验验证了FPMN的可行性和有效性。因此，FPMN可以明显降低对FPM实际应用的要求。
英文摘要：
Fourier ptychography microscopy(FP) is a recently developed computational imaging approach for microscopic super-resolution imaging. By turning on each light-emitting-diode (LED) located on different position on the LED array sequentially and acquiring the corresponding images that contain different spatial frequency components, high spatial resolution and quantitative phase imaging can be achieved in the case of large field-of-view. Nevertheless, FPM has high requirements for the system construction and data acquisition processes, such as precise LEDs position, accurate focusing and appropriate exposure time, which brings many limitations to its practical applications. In this paper, inspired by artificial neural network, we propose a Fourier ptychography multi-parameter neural network (FPMN) with composite physical prior optimization. A hybrid parameter determination strategy combining physical imaging model and data-driven network training is proposed to recover the multi layers of the network corresponding to different physical parameters, including sample complex function, system pupil function, defocus distance, LED array position deviation and illumination intensity fluctuation, etc. Among these parameters, LED array position deviation is recovered based on the features of brightfield to darkfield transition low-resolution images while the others are recovered in the process of training of the neural network. The feasibility and effectiveness of FPMN are verified through simulations and actual experiments. Therefore FPMN can evidently reduce the requirement for practical applications of FPM.
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