Imaging through highly dynamic thick turbid media based on neural network

Shanshan Zheng; Hao Wang; Dong Shi; Guohai Situ

doi:10.1117/12.2575045

10 October 2020 Imaging through highly dynamic thick turbid media based on neural network

Shanshan Zheng, Hao Wang, Dong Shi, Guohai Situ

Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 115500D (2020) https://doi.org/10.1117/12.2575045
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Imaging through the dynamic scattering media is a challenging problem in various situations like imaging under dense fog or turbid water. Here, we use fat emulsion suspensions as optical phantoms to mimic the turbid media and propose a single-shot end-to-end learning based method to directly retrieve the objects from the corresponding scattering images. We present the measurement of the dynamic characteristics of Intralipid dilutions, including optical thickness and decorrelation time. And a glass jar with a length 33.6cm is used in our incoherent imaging system, where the background noise is also existed. Experimental results show that our approach can reconstruct the object almost perfectly under the strong background light circumstance, where the signal-noise ratio is lower than -17 dB and the optical depth is close to 16.

Conference Presentation

Citation Download Citation

Shanshan Zheng, Hao Wang, Dong Shi, and Guohai Situ "Imaging through highly dynamic thick turbid media based on neural network", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 115500D (10 October 2020); https://doi.org/10.1117/12.2575045

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