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Proceedings Paper

Cross-modality deep learning brings bright-field image contrast to digital holographic microscopy (Conference Presentation)

Paper Abstract

We demonstrate a deep learning-based hologram reconstruction method that achieves bright-field microscopy image contrast in digital holographic microscopy (DHM), which we termed as “bright-field holography”. In bright-field holography, a generative adversarial network was trained to transform a complex-valued DHM reconstruction (obtained without phase-retrieval) into an equivalent image captured by a high-NA bright-field microscope, corresponding to the same sample plane. As a proof-of-concept, we demonstrated snapshot imaging of pollen samples distributed in 3D, digitally matching the contrast and shallow depth-of-field advantages of bright-field microscopy; this enabled us to digitally image a sample volume using bright-field holography without any physical axial scanning.

Paper Details

Date Published: 11 March 2020
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Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490R (11 March 2020); doi: 10.1117/12.2546880
Show Author Affiliations
Yichen Wu, Univ. of California, Los Angeles (United States)
Yilin Luo, Univ. of California, Los Angeles (United States)
Gunvant Chaudhari, Univ. of California, Los Angeles (United States)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Ayfer Calis, Univ. of California, Los Angeles (United States)
Kevin de Haan, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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