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Fourier ptychography microscopy(FP) is a recently developed computationalimaging approach for microscopic super-resolution imaging. By turning on eachlight-emitting-diode (LED) located on different position on the LED arraysequentially and acquiring the corresponding images that contain differentspatial frequency components, high spatial resolution and quantitative phaseimaging can be achieved in the case of large field-of-view. Nevertheless, FPMhas high requirements for the system construction and data acquisitionprocesses, such as precise LEDs position, accurate focusing and appropriateexposure time, which brings many limitations to its practical applications. Inthis paper, inspired by artificial neural network, we propose a Fourierptychography multi-parameter neural network (FPMN) with composite physicalprior optimization. A hybrid parameter determination strategy combiningphysical imaging model and data-driven network training is proposed to recoverthe multi layers of the network corresponding to different physical parameters,including sample complex function, system pupil function, defocus distance, LEDarray position deviation and illumination intensity fluctuation, etc. Amongthese parameters, LED array position deviation is recovered based on thefeatures of brightfield to darkfield transition low-resolution images while theothers are recovered in the process of training of the neural network. Thefeasibility and effectiveness of FPMN are verified through simulations andactual experiments. Therefore FPMN can evidently reduce the requirement forpractical applications of FPM.

Fourier ptychography multi-parameter neural network with composite physical priori optimization