Deep neural network for hologram reconstruction with superior external generalization

A system for performing phase recovery and holographic image reconstruction from holographic amplitude images of a test sample comprising:

• a microscopy device having a light source and an image sensor, the image sensor configured to capture one or more holographic amplitude images of the test sample at one or more sample-to-sensor distances;
• and
• a computing device comprising one or more processors configured to execute software containing a trained deep neural network, wherein the trained deep neural network comprises a plurality of trained spatial Fourier transform (SPAF) modules, each SPAF module configured to perform a two-dimensional (2D) Discrete Fourier transform from a spatial domain to a frequency domain followed by a linear transformation in the frequency domain followed by an inverse 2D Discrete Fourier transform to the spatial domain, wherein the trained deep neural network is trained with holographic amplitude images obtained of a plurality of training samples at one or more sample-to-sensor distances and their corresponding in-focus phase-recovered ground truth images to optimize learnable parameters in the deep neural network, wherein the trained deep neural network is configured to receive the one or more holographic amplitude images of the test sample obtained with the image sensor and outputs an in-focus complex-valued output image of the test sample that includes phase information and amplitude information of the test sample.