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231223s1999 xx |||||o 00| ||eng c |
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|a 10.1109/83.799895
|2 doi
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|a eng
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| 100 |
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|a Zhu, W
|e verfasserin
|4 aut
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|a Regularized total least squares approach for nonconvolutional linear inverse problems
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|c 1999
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 11.12.2009
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|a Date Revised 12.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a In this correspondence, a solution is developed for the regularized total least squares (RTLS) estimate in linear inverse problems where the linear operator is nonconvolutional. Our approach is based on a Rayleigh quotient (RQ) formulation of the TLS problem, and we accomplish regularization by modifying the RQ function to enforce a smooth solution. A conjugate gradient algorithm is used to minimize the modified RQ function. As an example, the proposed approach has been applied to the perturbation equation encountered in optical tomography. Simulation results show that this method provides more stable and accurate solutions than the regularized least squares and a previously reported total least squares approach, also based on the RQ formulation
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|a Letter
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|a Wang, Y
|e verfasserin
|4 aut
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1 |
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|a Galatsanos, N P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, J
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 8(1999), 11 vom: 28., Seite 1657-61
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
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|g volume:8
|g year:1999
|g number:11
|g day:28
|g pages:1657-61
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|u http://dx.doi.org/10.1109/83.799895
|3 Volltext
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|d 8
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