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Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography

Shaw, Calvin B and Yalavarthy, Phaneendra K (2014) Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography. In: JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 31 (4). pp. 852-862.

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Official URL: http://dx.doi.org/10.1364/JOSAA.31.000852

Abstract

The sparse estimation methods that utilize the l(p)-norm, with p being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These l(p)-norm-based regularizations make the optimization function nonconvex, and algorithms that implement l(p)-norm minimization utilize approximations to the original l(p)-norm function. In this work, three such typical methods for implementing the l(p)-norm were considered, namely, iteratively reweighted l(1)-minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of l(p)-minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. (C) 2014 Optical Society of America

Item Type: Journal Article
Additional Information: Copyright for this article belongs to the OPTICAL SOC AMER, USA
Department/Centre: Division of Interdisciplinary Research > Supercomputer Education & Research Centre
Depositing User: Id for Latest eprints
Date Deposited: 19 May 2014 05:27
Last Modified: 19 May 2014 05:27
URI: http://eprints.iisc.ac.in/id/eprint/49099

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