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11 March 2020 Cross-modality deep learning brings bright-field image contrast to digital holographic microscopy (Conference Presentation)
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 112490R (2020) https://doi.org/10.1117/12.2546880
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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Yichen Wu, Yilin Luo, Gunvant Chaudhari, Yair Rivenson, Ayfer Calis, Kevin de Haan, and Aydogan Ozcan "Cross-modality deep learning brings bright-field image contrast to digital holographic microscopy (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490R (11 March 2020); https://doi.org/10.1117/12.2546880
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