Macroscopic Fourier Ptychographic Imaging Based on Deep Learning

Fourier Ptychography (FP) is a powerful computational imaging technique that enables high-resolution, wide-field imaging by synthesizing apertures and leveraging coherent diffraction. However, the application of FP in long-distance imaging has been limited due to challenges such as noise and optical aberrations. This study introduces deep learning methods following macroscopic FP to further enhance image quality. Specifically, we employ super-resolution convolutional neural networks and very deep super-resolution, incorporating residual learning and residual neural network architectures to optimize network performance. These techniques significantly improve the resolution and clarity of FP images. Experiments with real-world film samples demonstrate the effectiveness of the proposed methods in practical applications. This research highlights the potential of deep learning to advance computational imaging techniques like FP, paving the way for improved long-distance imaging capabilities.