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.
@article{arxiv.1907.06727,
title = {Deep learning-based color holographic microscopy},
author = {Tairan Liu and Zhensong Wei and Yair Rivenson and Kevin de Haan and Yibo Zhang and Yichen Wu and Aydogan Ozcan},
journal= {arXiv preprint arXiv:1907.06727},
year = {2019}
}