Parameter selection methods for axisymmetric flame tomography through Tikhonov regularization
(2008) In Applied Optics 47(3). p.407-416- Abstract
- Deconvolution of optically collected axisymmetric flame data is equivalent to solving an ill-posed problem subject to severe error amplification. Tikhonov regularization has recently been shown to be well suited for stabilizing this deconvolution, although the success of this method hinges on choosing a suitable regularization parameter. Incorporating a parameter selection scheme transforms this technique into a reliable automatic algorithm that outperforms unregularized deconvolution of a smoothed data set, which is currently the most popular way to analyze axisymmetric data. We review the discrepancy principle, L-curve curvature, and generalized cross-validation parameter selection schemes and conclude that the L-curve curvature... (More)
- Deconvolution of optically collected axisymmetric flame data is equivalent to solving an ill-posed problem subject to severe error amplification. Tikhonov regularization has recently been shown to be well suited for stabilizing this deconvolution, although the success of this method hinges on choosing a suitable regularization parameter. Incorporating a parameter selection scheme transforms this technique into a reliable automatic algorithm that outperforms unregularized deconvolution of a smoothed data set, which is currently the most popular way to analyze axisymmetric data. We review the discrepancy principle, L-curve curvature, and generalized cross-validation parameter selection schemes and conclude that the L-curve curvature algorithm is best suited to this problem. (C) 2008 Optical Society of America. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1196996
- author
- Åkesson, Emil O LU and Daun, Kyle J
- organization
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Applied Optics
- volume
- 47
- issue
- 3
- pages
- 407 - 416
- publisher
- Optical Society of America
- external identifiers
-
- wos:000253266800012
- scopus:41549119442
- ISSN
- 2155-3165
- DOI
- 10.1364/AO.47.000407
- language
- English
- LU publication?
- yes
- id
- 001349a6-9b75-4a1e-8139-bbfefe6853b3 (old id 1196996)
- date added to LUP
- 2016-04-01 12:04:21
- date last changed
- 2022-03-13 04:55:37
@article{001349a6-9b75-4a1e-8139-bbfefe6853b3,
abstract = {{Deconvolution of optically collected axisymmetric flame data is equivalent to solving an ill-posed problem subject to severe error amplification. Tikhonov regularization has recently been shown to be well suited for stabilizing this deconvolution, although the success of this method hinges on choosing a suitable regularization parameter. Incorporating a parameter selection scheme transforms this technique into a reliable automatic algorithm that outperforms unregularized deconvolution of a smoothed data set, which is currently the most popular way to analyze axisymmetric data. We review the discrepancy principle, L-curve curvature, and generalized cross-validation parameter selection schemes and conclude that the L-curve curvature algorithm is best suited to this problem. (C) 2008 Optical Society of America.}},
author = {{Åkesson, Emil O and Daun, Kyle J}},
issn = {{2155-3165}},
language = {{eng}},
number = {{3}},
pages = {{407--416}},
publisher = {{Optical Society of America}},
series = {{Applied Optics}},
title = {{Parameter selection methods for axisymmetric flame tomography through Tikhonov regularization}},
url = {{http://dx.doi.org/10.1364/AO.47.000407}},
doi = {{10.1364/AO.47.000407}},
volume = {{47}},
year = {{2008}},
}