Total variation optimization for imaging through turbid media with transmission matrix

Changmei Gong; Xiaopeng Shao; Tengfei Wu; Jietao Liu; Jianqi Zhang

doi:10.1117/1.OE.55.12.121703

11 May 2016 Total variation optimization for imaging through turbid media with transmission matrix

Changmei Gong, Xiaopeng Shao, Tengfei Wu, Jietao Liu, Jianqi Zhang

Author Affiliations +

Optical Engineering, Vol. 55, Issue 12, 121703 (May 2016). https://doi.org/10.1117/1.OE.55.12.121703

Abstract

With the transmission matrix (TM) of the whole optical system measured, the image of the object behind a turbid medium can be recovered from its speckle field by means of an image reconstruction algorithm. Instead of Tikhonov regularization algorithm (TRA), the total variation minimization by augmented Lagrangian and alternating direction algorithms (TVAL3) is introduced to recover object images. As a total variation (TV)-based approach, TVAL3 allows to effectively damp more noise and preserve more edges compared with TRA, thus providing more outstanding image quality. Different levels of detector noise and TM-measurement noise are successively added to analyze the antinoise performance of these two algorithms. Simulation results show that TVAL3 is able to recover more details and suppress more noise than TRA under different noise levels, thus providing much more excellent image quality. Furthermore, whether it be detector noise or TM-measurement noise, the reconstruction images obtained by TVAL3 at SNR=15 dB are far superior to those by TRA at SNR=50 dB.

Citation Download Citation

Changmei Gong, Xiaopeng Shao, Tengfei Wu, Jietao Liu, and Jianqi Zhang "Total variation optimization for imaging through turbid media with transmission matrix," Optical Engineering 55(12), 121703 (11 May 2016). https://doi.org/10.1117/1.OE.55.12.121703

Published: 11 May 2016

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