FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representation

Fourier ptychographic microscopy (FPM) has its strength in tackling the trade-off between resolution and field-of-view of imaging systems by computational methods. Here, we present a time-efficient and physics-based algorithm for FPM image stack reconstruction using implicit neural representation and tensor low-rank approximation. The method is free of any pre-training process and can be easily adapted to various computational microscopes. Compared to the conventional FPM methods for image stack reconstruction, the proposed method can be several times faster than conventional FPM methods on the same graphics processing units (GPU) and significantly reduce data volume for storage. The proposed method has potential applications in digital pathology and its downstream data-driven tasks, and can be beneficial to data collaboration in biological sciences.