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Proposal of fault-tolerant tomographic image reconstruction
Author(s): Hiroyuki Kudo; Keita Takaki; Fukashi Yamazaki; Takuya Nemoto
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Paper Abstract

This paper deals with tomographic image reconstruction under the situation where some of projection data bins are contaminated with abnormal data. Such situations occur in various instances of tomography. We propose a new reconstruction algorithm called the Fault-Tolerant reconstruction outlined as follows. The least-squares (L2- norm) error function || Ax- b||22 used in ordinary iterative reconstructions is sensitive to the existence of abnormal data. The proposed algorithm utilizes the L1-norm error function || Ax- b||11 instead of the L2-norm, and we develop a row-action-type iterative algorithm using the proximal splitting framework in convex optimization fields. We also propose an improved version of the L1-norm reconstruction called the L1-TV reconstruction, in which a weak Total Variation (TV) penalty is added to the cost function. Simulation results demonstrate that reconstructed images with the L2-norm were severely damaged by the effect of abnormal bins, whereas images with the L1-norm and L1-TV reconstructions were robust to the existence of abnormal bins.

Paper Details

Date Published: 3 October 2016
PDF: 12 pages
Proc. SPIE 9967, Developments in X-Ray Tomography X, 99671K (3 October 2016); doi: 10.1117/12.2237107
Show Author Affiliations
Hiroyuki Kudo, Univ. of Tsukuba (Japan)
JST-ERATO, Momose Quantum-Beam Phase Imaging Project (Japan)
Keita Takaki, Univ. of Tsukuba (Japan)
Fukashi Yamazaki, Univ. of Tsukuba (Japan)
Takuya Nemoto, Univ. of Tsukuba (Japan)

Published in SPIE Proceedings Vol. 9967:
Developments in X-Ray Tomography X
Stuart R. Stock; Bert Müller; Ge Wang, Editor(s)

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