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Optical Engineering

Total variation optimization for imaging through turbid media with transmission matrix
Author(s): Changmei Gong; Xiaopeng Shao; Tengfei Wu; Jietao Liu; Jianqi Zhang
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Paper 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.

Paper Details

Date Published: 11 May 2016
PDF: 9 pages
Opt. Eng. 55(12) 121703 doi: 10.1117/1.OE.55.12.121703
Published in: Optical Engineering Volume 55, Issue 12
Show Author Affiliations
Changmei Gong, Xidian Univ. (China)
Xiaopeng Shao, Xidian Univ. (China)
Tengfei Wu, Xidian Univ. (China)
Jietao Liu, Xidian Univ. (China)
Jianqi Zhang, Xidian Univ. (China)

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