Deep learning in computational imaging

Jianglei Di; Kaiqiang Wang; Jianlin Zhao

doi:10.1117/12.2573707

10 October 2020 Deep learning in computational imaging

Jianglei Di, Kaiqiang Wang, Jianlin Zhao

Proceedings Volume 11551, Holography, Diffractive Optics, and Applications X; 1155107 (2020) https://doi.org/10.1117/12.2573707
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Deep learning has great potential in computational imaging. We propose to use three kinds of artificial neural networks in phase imaging works. An improved U-net is used to do phase unwrapping with a new phase dataset generation method and do phase imaging in an optical microscope with Transport of Intensity Equation (TIE). And then, Y-Net and Y4-Net are used to do single-wavelength and dual-wavelength digital holographic reconstruction, respectively.

Conference Presentation

Citation Download Citation

Jianglei Di, Kaiqiang Wang, and Jianlin Zhao "Deep learning in computational imaging", Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 1155107 (10 October 2020); https://doi.org/10.1117/12.2573707

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