Deep neural network for hologram reconstruction with superior external generalization

Abstract

A deep learning framework, termed Fourier Imager Network (FIN) is disclosed that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples, exhibiting success in external generalization. The FIN architecture is based on spatial Fourier transform modules with the deep neural network that process the spatial frequencies of its inputs using learnable filters and a global receptive field. FIN exhibits superior generalization to new types of samples, while also being much faster in its image inference speed, completing the hologram reconstruction task in ˜0.04 s per 1 mmof the sample area. Beyond holographic microscopy and quantitative phase imaging applications, FIN and the underlying neural network architecture may open up various new opportunities to design broadly generalizable deep learning models in computational imaging and machine vision fields.