Phase recovery and holographic image reconstruction using deep learning in neural networks

TL;DR

This paper introduces a deep learning approach for phase recovery and holographic image reconstruction that is faster and requires fewer measurements than traditional methods, demonstrated on biological samples.

Contribution

The authors develop a neural network framework that efficiently performs phase recovery and holography from a single hologram, surpassing existing techniques in speed and accuracy.

Findings

Neural network successfully reconstructs phase and amplitude images from one hologram.

The method is significantly faster than traditional algorithms.

Validated on biological samples like blood and tissue sections.

Abstract

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference related spatial artifacts. Compared to existing approaches, this neural network based method is significantly faster to compute, and reconstructs improved phase and amplitude images of the objects using only one hologram, i.e., requires less number of measurements in addition to being computationally faster. We validated this method by reconstructing phase and amplitude images of various samples, including blood and Pap smears, and tissue sections. These results are broadly applicable to any phase recovery problem, and highlight that through machine learning challenging problems in imaging science can be overcome, providing new avenues to design powerful computational imaging systems.