Abstract

In this paper, we propose a dual-structured prior neural network model that independently restores both the amplitude and phase image using a random latent code for Fourier ptychography (FP). We demonstrate that the inherent prior information within the neural network can generate super-resolution images with a resolution that exceeds the combined numerical aperture of the FP system. This method circumvents the need for a large labeled dataset. The training process is guided by an appropriate forward physical model. We validate the effectiveness of our approach through simulations and experimental data. The results suggest that integrating image prior information with system-collected data is a potentially effective approach for improving the resolution of FP systems.

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