Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network
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optica-9-10-1149.pdf
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Published version
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23.54 MB
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Author(s)
Kang, Iksung
de Cea, Marc
Xue, Jin
Li, Zheng
Barbastathis, George
Ram, Rajeev J
Date Issued
2022
Journal
Optica
Publisher
Optica Publishing Group
Citation
Kang, Iksung, de Cea, Marc, Xue, Jin, Li, Zheng, Barbastathis, George et al. 2022. "Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network." Optica, 9 (10).
Version
Final published version
Abstract
Lensless holography promises compact, low-cost optical apparatus
designs with similar performance to traditional imaging setups. Here, we propose the use of a silicon micro-LED fabricated in a commercial CMOS microelectronics process as the illumination source in a lensless holographic microscope. Its small emission area ( < 4 µ m 2 ) ensures high spatial coherence without the need for a pinhole and results in a large NA setup, circumventing the limits to the source-to-sample distance encountered by conventional lensless holography apparatus. The scene is reconstructed using an untrained deep neural network architecture that simultaneously performs spectral recovery by learning from the given single experimental diffraction intensity. We envision this synergetic combination of CMOS micro-LEDs and the machine learning framework can be used in other computational imaging applications, such as a compact microscope for live-cell tracking or spectroscopic imaging of biological materials.
designs with similar performance to traditional imaging setups. Here, we propose the use of a silicon micro-LED fabricated in a commercial CMOS microelectronics process as the illumination source in a lensless holographic microscope. Its small emission area ( < 4 µ m 2 ) ensures high spatial coherence without the need for a pinhole and results in a large NA setup, circumventing the limits to the source-to-sample distance encountered by conventional lensless holography apparatus. The scene is reconstructed using an untrained deep neural network architecture that simultaneously performs spectral recovery by learning from the given single experimental diffraction intensity. We envision this synergetic combination of CMOS micro-LEDs and the machine learning framework can be used in other computational imaging applications, such as a compact microscope for live-cell tracking or spectroscopic imaging of biological materials.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1364/OPTICA.470712
