Interpolation of sub-Nyquist sampled speckles with deep learning

Huanhao Li; Zhipeng Yu; Yunqi Luo; Yuanjin Zheng; Puxiang Lai

doi:10.1117/12.2573859

10 October 2020 Interpolation of sub-Nyquist sampled speckles with deep learning

Huanhao Li, Zhipeng Yu, Yunqi Luo, Yuanjin Zheng, Puxiang Lai

Proceedings Volume 11551, Holography, Diffractive Optics, and Applications X; 1155105 (2020) https://doi.org/10.1117/12.2573859
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Information retrieval from optical speckles is desired yet challenging. Insufficient sampling, especially in sub-Nyquist domain, of speckles significantly destroys the encoded information and correlations among these speckle grains. To address that, we trained a deep neural network to combat the physical imperfection: the sub-Nyquist sampled speckles (~14 below the Nyquist criterion) are interpolated up to a well-resolved level (322 times more pixels to resolve the same FOV) with smoothed morphology fine-textured, and more importantly, lost information retraced. With the FOV-resolution dilemma favorably overcome, it deepens our understanding of the scattering, enabling big and clear imaging in complex scenarios.

Conference Presentation

Citation Download Citation

Huanhao Li, Zhipeng Yu, Yunqi Luo, Yuanjin Zheng, and Puxiang Lai "Interpolation of sub-Nyquist sampled speckles with deep learning", Proc. SPIE 11551, Holography, Diffractive Optics, and Applications X, 1155105 (10 October 2020); https://doi.org/10.1117/12.2573859

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