Neural network model assisted Fourier ptychography with Zernike aberration recovery and total variation constraint

摘要

Significance: Fourier ptychography (FP) is a computational imaging approach that achieves high-resolution reconstruction. Inspired by neural networks, many deep-learning-based methods are proposed to solve FP problems. However, the performance of FP still suffers from optical aberration, which needs to be considered.