  Fourier ptychography (FP) is a newly developed computational imaging approach
that achieves both high resolution and wide field of view by stitching a series
of low-resolution images captured under angle-varied illumination. So far, many
supervised data-driven models have been applied to solve inverse imaging
problems. These models need massive amounts of data to train, and are limited
by the dataset characteristics. In FP problems, generic datasets are always
scarce, and the optical aberration varies greatly under different acquisition
conditions. To address these dilemmas, we model the forward physical imaging
process as an interpretable physics-guided neural network (PgNN), where the
reconstructed image in the complex domain is considered as the learnable
parameters of the neural network. Since the optimal parameters of the PgNN can
be derived by minimizing the difference between the model-generated images and
real captured angle-varied images corresponding to the same scene, the proposed
PgNN can get rid of the problem of massive training data as in traditional
supervised methods. Applying the alternate updating mechanism and the total
variation regularization, PgNN can flexibly reconstruct images with improved
performance. In addition, the Zernike mode is incorporated to compensate for
optical aberrations to enhance the robustness of FP reconstructions. As a
demonstration, we show our method can reconstruct images with smooth
performance and detailed information in both simulated and experimental
datasets. In particular, when validated in an extension of a high-defocus,
high-exposure tissue section dataset, PgNN outperforms traditional FP methods
with fewer artifacts and distinguishable structures.
