  Deep learning-based image reconstruction methods have achieved remarkable
success in phase recovery and holographic imaging. However, the generalization
of their image reconstruction performance to new types of samples never seen by
the network remains a challenge. Here we introduce a deep learning framework,
termed Fourier Imager Network (FIN), that can perform end-to-end phase recovery
and image reconstruction from raw holograms of new types of samples, exhibiting
unprecedented success in external generalization. FIN architecture is based on
spatial Fourier transform modules that process the spatial frequencies of its
inputs using learnable filters and a global receptive field. Compared with
existing convolutional deep neural networks used for hologram reconstruction,
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 mm^2 of the sample area. We experimentally
validated the performance of FIN by training it using human lung tissue samples
and blindly testing it on human prostate, salivary gland tissue and Pap smear
samples, proving its superior external generalization and image reconstruction
speed. Beyond holographic microscopy and quantitative phase imaging, FIN and
the underlying neural network architecture might open up various new
opportunities to design broadly generalizable deep learning models in
computational imaging and machine vision fields.
