Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning

Fourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors such as noise, optical aberration, and phase wrapping. In this work, we propose a semi-supervised Fourier ptychographic transformer network (SFPT) for improved image reconstruction, which employs a two-stage training approach to enhance the image quality. First, self-supervised learning guided by low-resolution amplitudes and Zernike modes is utilized to recover pupil function. Second, a supervised learning framework with augmented training datasets is applied to further refine reconstruction quality. Moreover, the unwrapped phase is recovered by adjusting the phase distribution range in the augmented training datasets. The effectiveness of the proposed method is validated by using both the simulation and experimental data. This deep-learning-based method has potential applications for imaging thicker biology samples.