  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.
