Learnable-pattern scanning based deep compressed imaging

Kangning Zhang; Junjie Hu; Weijian Yang

doi:10.1117/12.2577998

5 March 2021 Learnable-pattern scanning based deep compressed imaging

Kangning Zhang, Junjie Hu, Weijian Yang

Author Affiliations +

Proceedings Volume 11654, High-Speed Biomedical Imaging and Spectroscopy VI; 1165413 (2021) https://doi.org/10.1117/12.2577998
Event: SPIE BiOS, 2021, Online Only

Abstract

We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality.

Conference Presentation

Citation Download Citation

Kangning Zhang, Junjie Hu, and Weijian Yang "Learnable-pattern scanning based deep compressed imaging", Proc. SPIE 11654, High-Speed Biomedical Imaging and Spectroscopy VI, 1165413 (5 March 2021); https://doi.org/10.1117/12.2577998

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