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Proceedings Paper

An adaptive total variation image reconstruction method for speckles through disordered media
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Paper Abstract

Multiple scattering of light in highly disordered medium can break the diffraction limit of conventional optical system combined with image reconstruction method. Once the transmission matrix of the imaging system is obtained, the target image can be reconstructed from its speckle pattern by image reconstruction algorithm. Nevertheless, the restored image attained by common image reconstruction algorithms such as Tikhonov regularization has a relatively low signal-tonoise ratio (SNR) due to the experimental noise and reconstruction noise, greatly reducing the quality of the result image. In this paper, the speckle pattern of the test image is simulated by the combination of light propagation theories and statistical optics theories. Subsequently, an adaptive total variation (ATV) algorithm—the TV minimization by augmented Lagrangian and alternating direction algorithms (TVAL3), which is based on augmented Lagrangian and alternating direction algorithm, is utilized to reconstruct the target image. Numerical simulation experimental results show that, the TVAL3 algorithm can effectively suppress the noise of the restored image and preserve more image details, thus greatly boosts the SNR of the restored image. It also indicates that, compared with the image directly formed by ‘clean’ system, the reconstructed results can overcoming the diffraction limit of the ‘clean’ system, therefore being conductive to the observation of cells and protein molecules in biological tissues and other structures in micro/nano scale.

Paper Details

Date Published: 17 September 2013
PDF: 7 pages
Proc. SPIE 8877, Unconventional Imaging and Wavefront Sensing 2013, 88770C (17 September 2013);
Show Author Affiliations
Changmei Gong, Xidian Univ. (China)
Xiaopeng Shao, Xidian Univ. (China)
Tengfei Wu, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 8877:
Unconventional Imaging and Wavefront Sensing 2013
Jean J. Dolne; Thomas J. Karr; Victor L. Gamiz, Editor(s)

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