Deep learning-based color holographic microscopy

TL;DR

This paper introduces a GAN-based framework for high-fidelity color holographic microscopy that reconstructs accurate color images from a single hologram, improving speed and efficiency in histopathology imaging.

Contribution

It presents a novel deep learning approach that eliminates artifacts and enables accurate color reconstruction from a single hologram, advancing holographic microscopy capabilities.

Findings

Successful experimental demonstration on tissue samples

Significant reduction in imaging artifacts

Single hologram suffices for accurate color reconstruction

Abstract

We report a framework based on a generative adversarial network (GAN) that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network learns to eliminate missing-phase-related artifacts, and generates an accurate color transformation for the reconstructed image. Our framework is experimentally demonstrated using lung and prostate tissue sections that are labeled with different histological stains. This framework is envisaged to be applicable to point-of-care histopathology, and presents a significant improvement in the throughput of coherent microscopy systems given that only a single hologram of the specimen is required for accurate color imaging.