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Proceedings Paper

Multiresolution regularized least squares image reconstruction based on wavelet in optical tomography
Author(s): Wenwu Zhu; Yao Wang; Yining Deng; Yuqi Yao; Randall Locke Barbour
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Paper Abstract

In this paper, we present a wavelet based multigrid approach to solve the perturbation equation encountered in optical tomogrpahy. With this scheme, the unkown image, the data, as well as weight matrix are all represented by wavelet expansions, and thus yielding a multiresolution representation of the original perturbation equation in the wavelet domain. This transformed equation is then solved using multigrid scheme, by which an increasing portion of wavelet coefficients of the unknown image are solved in successive approximations. One can also quickly identify regions of interest from a coarse level reconstruction and restrict the reconstruction in the following fine resolutions to those regions. At each resolution level, a regularized least squares solution is obtained using a conjugate gradient descent method. Compared to a previously reported one grid algorithm, the multigrid method requires substantially shorter computation time under the same reconstruction quality criterion.

Paper Details

Date Published: 9 October 1995
PDF: 11 pages
Proc. SPIE 2570, Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications, (9 October 1995); doi: 10.1117/12.224161
Show Author Affiliations
Wenwu Zhu, Polytechnic Univ. (United States)
Yao Wang, Polytechnic Univ. (United States)
Yining Deng, Polytechnic Univ. (United States)
Yuqi Yao, Polytechnic Univ. (United States)
Randall Locke Barbour, SUNY Health Science Ctr./Brooklyn (United States)


Published in SPIE Proceedings Vol. 2570:
Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)

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