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Optical Engineering

Comprehensive study of methods for automatic choice of regularization parameter for diffuse optical tomography
Author(s): Zhonghua Sun; Yaqi Wang; Kebin Jia; Jinchao Feng
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Paper Abstract

The image reconstruction in diffuse optical tomography (DOT) is a typical inverse problem; therefore, regularization techniques are essential to obtain a reliable solution. The most general form of regularization is Tikhonov regularization. With any Tikhonov regularized reconstruction algorithm, one of the crucial issues is the selection of the regularization parameter that controls the trade-off between the regularized solution and fidelity to the given sets of data. Automatic methods such as L-curve, generalized cross-validation, minimal residual method, projection error method, and model function method have been introduced to select the regularization parameter over the years. However, little investigation of comparison of all the algorithms has been reported in DOT. The performance of the five methods for choosing regularization parameter is comprehensively compared, and advantages and limitations are discussed.

Paper Details

Date Published: 23 December 2016
PDF: 10 pages
Opt. Eng. 56(4) 041310 doi: 10.1117/1.OE.56.4.041310
Published in: Optical Engineering Volume 56, Issue 4
Show Author Affiliations
Zhonghua Sun, Beijing Univ. of Technology (China)
Beijing Lab. of Advanced Information Networks (China)
Yaqi Wang, Beijing Univ. of Technology (China)
Beijing Lab. of Advanced Information Networks (China)
Kebin Jia, Beijing Univ. of Technology (China)
Beijing Lab. of Advanced Information Networks (China)
Jinchao Feng, Beijing Univ. of Technology (China)
Beijing Lab. of Advanced Information Networks (China)


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