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Quantitative microwave tomography from sparse measurements using a robust huber regularizer

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Fwo microwave tomography
Abstract
In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered fields, which is a nonlinear, ill-posed inverse problem. The results on 3D data sets are encouraging: the reconstruction error is reduced and the permittivity profile can be estimated from fewer measurements compared to state-of-the art inversion procedures.
Keywords
inverse problem, robust estimation, microwave imaging, regularization.

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Citation

Please use this url to cite or link to this publication:

Chicago
Bai, Funing, Aleksandra Pizurica, Sam Vanloocke, Ann Franchois, Daniël De Zutter, and Wilfried Philips. 2012. “Quantitative Microwave Tomography from Sparse Measurements Using a Robust Huber Regularizer.” In IEEE International Conference on Image Processing, Proceedings, 2073–2076. Piscataway, NJ, USA: IEEE.
APA
Bai, F., Pizurica, A., Vanloocke, S., Franchois, A., De Zutter, D., & Philips, W. (2012). Quantitative microwave tomography from sparse measurements using a robust huber regularizer. IEEE International Conference on Image Processing, Proceedings (pp. 2073–2076). Presented at the IEEE International Conference on Image Processing (ICIP - 2012), Piscataway, NJ, USA: IEEE.
Vancouver
1.
Bai F, Pizurica A, Vanloocke S, Franchois A, De Zutter D, Philips W. Quantitative microwave tomography from sparse measurements using a robust huber regularizer. IEEE International Conference on Image Processing, Proceedings. Piscataway, NJ, USA: IEEE; 2012. p. 2073–6.
MLA
Bai, Funing, Aleksandra Pizurica, Sam Vanloocke, et al. “Quantitative Microwave Tomography from Sparse Measurements Using a Robust Huber Regularizer.” IEEE International Conference on Image Processing, Proceedings. Piscataway, NJ, USA: IEEE, 2012. 2073–2076. Print.
@inproceedings{2090282,
  abstract     = {In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative microwave tomography aims at estimating the permittivity profile of a scattering object based on measured scattered fields, which is a nonlinear, ill-posed inverse problem. The results on 3D data sets are encouraging: the reconstruction error is reduced and the permittivity profile can be estimated from fewer measurements compared to state-of-the art inversion procedures.},
  author       = {Bai, Funing and Pizurica, Aleksandra and Vanloocke, Sam and Franchois, Ann and De Zutter, Dani{\"e}l and Philips, Wilfried},
  booktitle    = {IEEE International Conference on Image Processing, Proceedings},
  isbn         = {9781467325325},
  issn         = {1522-4880},
  keyword      = {inverse problem,robust estimation,microwave imaging,regularization.},
  language     = {eng},
  location     = {Orlando, Florida, USA},
  pages        = {2073--2076},
  publisher    = {IEEE},
  title        = {Quantitative microwave tomography from sparse measurements using a robust huber regularizer},
  year         = {2012},
}

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