  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.
