High-efficiency imaging through scattering media in noisy environments via sparse image reconstruction

Tengfei Wu; Xiaopeng Shao; Changmei Gong; Huijuan Li; Jietao Liu

doi:10.1117/1.OE.54.11.113111

30 November 2015 High-efficiency imaging through scattering media in noisy environments via sparse image reconstruction

Tengfei Wu, Xiaopeng Shao, Changmei Gong, Huijuan Li, Jietao Liu

Author Affiliations +

Optical Engineering, Vol. 54, Issue 11, 113111 (November 2015). https://doi.org/10.1117/1.OE.54.11.113111

Abstract

High-efficiency imaging through highly scattering media is urgently desired for various applications. Imaging speed and imaging quality, which determine the imaging efficiency, are two inevitable indices for any optical imaging area. Based on random walk analysis in statistical optics, the elements in a transmission matrix (TM) actually obey Gaussian distribution. Instead of dealing with large amounts of data contained in TM and speckle pattern, imaging can be achieved with only a small number of the data via sparse representation. We make a detailed mathematical analysis of the elements-distribution of the TM of a scattering imaging system and study the imaging method of sparse image reconstruction (SIR). More specifically, we focus on analyzing the optimum sampling rates for the imaging of different structures of targets, which significantly influences both imaging speed and imaging quality. Results show that the optimum sampling rate exists in any noise-level environment if a target can be sparsely represented, and by searching for the optimum sampling rate, it can effectively balance the imaging quality and the imaging speed, which can maximize the imaging efficiency. This work is helpful for practical applications of imaging through highly scattering media with the SIR method.

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Tengfei Wu, Xiaopeng Shao, Changmei Gong, Huijuan Li, and Jietao Liu "High-efficiency imaging through scattering media in noisy environments via sparse image reconstruction," Optical Engineering 54(11), 113111 (30 November 2015). https://doi.org/10.1117/1.OE.54.11.113111

Published: 30 November 2015

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