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20 August 2020 3D reconstruction of a hologram with brightfield contrast using deep learning
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Holographic microscopy encodes the 3D information of a sample into a single hologram. However, holographic images are in general inferior to bright-field microscopy images in terms of contrast and signal-to-noise ratio, due to twin-image artifacts, speckle and out-of-plane interference. The contrast and noise problem of holography can be mitigated using iterative algorithms, but at the cost of additional measurements and time. Here, we present a deep-learning-based cross-modality imaging method to reconstruct a single hologram into volumetric images of a sample with bright-field contrast and SNR, merging the snapshot 3D imaging capability of holography with the image quality of bright-field microscopy.
Conference Presentation
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Yichen Wu, Yilin Luo, Gunvant Chaudhari, Yair Rivenson, Kevin De Haan, Ayfer Calis, and Aydogan Ozcan "3D reconstruction of a hologram with brightfield contrast using deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146919 (20 August 2020);

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