Deep learning-based color holographic microscopy

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 a...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Liu, Tairan, Zhensong Wei, Rivenson, Yair, de Haan, Kevin, Zhang, Yibo, Wu, Yichen, Ozcan, Aydogan
Format: Article
Language:English
Subjects:
Color imagery
Deep learning
Hardware reviews
Image reconstruction
Light
Microscopy
Prostate
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1907.06727