傅里叶叠层成像技术(FP)可重构出宽视场、高分辨率的物体幅值和相位分布,随着深度学习技术的不断发展,神经网络已成为求解计算成像中非线性逆问题的重要手段之一。针对FP系统数据特异性强、数据量少等特点,提出了一种结合计算成像先验知识和深度学习的算法,设计了基于物理模型的神经网络框架,并对仿真样本进行了验证。此外,还搭建了远场透射系统,对宏观物体的图像序列进行FP重建验证。实验结果表明,该系统能用有限的仿真与真实数据集重构出高分辨率样本的复振幅分布,且对光学像差与背景噪声的鲁棒性较强。

Fourier ptychography (FP) can reconstruct the amplitude and phase distribution of objects with a wide field of view and high. With the continuous development of deep learning, neural network has become one of the important methods to deal with the nonlinear inverse problems in computational imaging. Aiming at the characteristics of FP system such as strong data specificity and small amount of data, this paper proposes an algorithm combining computational imaging prior knowledge and deep learning, to design a neural network framework based on physical model, and verifies it on simulation samples. Furthermore, a far-field transmission system is constructed to verify the FP reconstruction of image sequences of macroscopic objects. Experimental results show that the system can reconstruct the complex amplitude distributions of high-resolution samples using limited simulation and real data sets, with high robustness to optical aberration and background noise.