Learning-based free-of-distortion phase imaging from raw holograms in digital holographic microscopy

Raul Castaneda; Carlos Trujillo; Ana Doblas

doi:10.1117/12.2619050

30 May 2022 Learning-based free-of-distortion phase imaging from raw holograms in digital holographic microscopy

Raul Castaneda, Carlos Trujillo, Ana Doblas

Proceedings Volume PC12097, Big Data IV: Learning, Analytics, and Applications; PC1209705 (2022) https://doi.org/10.1117/12.2619050
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

One of the most common tools to recover the phase information of unstained microscopic translucent samples is Digital Holographic Microscopy (DHM). This imaging technique has a broad number of applications in biology and biomedicine. Nonetheless, to reconstruct an aberration-free phase image using DHM, a computationally demanding numerical process must be precisely executed. In this contribution, we present a generative adversarial network to fully compensate and reconstruct DHM holograms without the need for any computational process directly from the recorded hologram.

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Raul Castaneda, Carlos Trujillo, and Ana Doblas "Learning-based free-of-distortion phase imaging from raw holograms in digital holographic microscopy", Proc. SPIE PC12097, Big Data IV: Learning, Analytics, and Applications, PC1209705 (30 May 2022); https://doi.org/10.1117/12.2619050

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KEYWORDS

Digital holography

Holograms

Holography

Microscopy

Phase imaging

3D image reconstruction

Gallium nitride

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