SURE-Based Regularization Parameter Selection for TomoSAR Imaging via Maximum-Likelihood

2020 21st International Radar Symposium (IRS) ◽

10.23919/irs48640.2020.9253844 ◽

2020 ◽

Author(s):

Serafin Garcia Sergio ◽

Martin del Campo Becerra Gustavo ◽

Ortega Cisneros Susana ◽

Reigber Andreas

Keyword(s):

Maximum Likelihood ◽

Regularization Parameter ◽

Parameter Selection ◽

Selection For ◽

Regularization Parameter Selection

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L1/2 Regularization Sar Imaging Via Complex Image Data: Regularization Parameter Selection for Target Detection Task

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519138 ◽

2018 ◽

Author(s):

Jia-cheng Ni ◽

Qun Zhang ◽

Ling-hua Su ◽

Jia Liang ◽

Wen-jun Huo

Keyword(s):

Target Detection ◽

Regularization Parameter ◽

Parameter Selection ◽

Detection Task ◽

Complex Image ◽

Sar Imaging ◽

Selection For ◽

Regularization Parameter Selection ◽

Target Detection Task

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On the Regularization Parameter Selection for Sparse Code Learning in Electrical Source Separation

Adaptive and Natural Computing Algorithms - Lecture Notes in Computer Science ◽

10.1007/978-3-642-37213-1_29 ◽

2013 ◽

pp. 277-286 ◽

Author(s):

Marisa Figueiredo ◽

Bernardete Ribeiro ◽

Ana Maria de Almeida

Keyword(s):

Regularization Parameter ◽

Source Separation ◽

Parameter Selection ◽

Sparse Code ◽

Selection For ◽

Regularization Parameter Selection ◽

Electrical Source

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Regularization Parameter Selection for Penalized Empirical Likelihood Estimator

SSRN Electronic Journal ◽

10.2139/ssrn.3177880 ◽

2018 ◽

Author(s):

Tomohiro Ando ◽

Naoya Sueishi

Keyword(s):

Empirical Likelihood ◽

Regularization Parameter ◽

Parameter Selection ◽

Likelihood Estimator ◽

Selection For ◽

Regularization Parameter Selection ◽

Penalized Empirical Likelihood

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Multi-scale Total Variation with Automated Regularization Parameter Selection for Color Image Restoration

Lecture Notes in Computer Science - Scale Space and Variational Methods in Computer Vision ◽

10.1007/978-3-642-02256-2_23 ◽

2009 ◽

pp. 271-281 ◽

Author(s):

Yiqiu Dong ◽

Michael Hintermüller

Keyword(s):

Image Restoration ◽

Total Variation ◽

Color Image ◽

Regularization Parameter ◽

Parameter Selection ◽

Multi Scale ◽

Selection For ◽

Regularization Parameter Selection

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Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation

Inverse Problems ◽

10.1088/0266-5611/25/9/095005 ◽

2009 ◽

Vol 25 (9) ◽

pp. 095005 ◽

Author(s):

Johnathan M Bardsley ◽

John Goldes

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Regularization Parameter ◽

Likelihood Estimation ◽

Parameter Selection ◽

Selection Methods ◽

Ill Posed ◽

Regularization Parameter Selection

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Regularization parameter selection for Bayesian reconstruction of attenuation map

1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255) ◽

10.1109/nssmic.1998.773847 ◽

2002 ◽

Author(s):

V.Y. Panin ◽

G.L. Zeng ◽

G.T. Gullberg

Keyword(s):

Regularization Parameter ◽

Parameter Selection ◽

Bayesian Reconstruction ◽

Attenuation Map ◽

Selection For ◽

Regularization Parameter Selection

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Information complexity-based regularization parameter selection for solution of ill conditioned inverse problems

Inverse Problems ◽

10.1088/0266-5611/18/2/101 ◽

2002 ◽

Vol 18 (2) ◽

pp. L1-L9 ◽

Author(s):

A M Urmanov ◽

A V Gribok ◽

H Bozdogan ◽

J W Hines ◽

R E Uhrig

Keyword(s):

Inverse Problems ◽

Regularization Parameter ◽

Parameter Selection ◽

Information Complexity ◽

Selection For ◽

Regularization Parameter Selection

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Regularization parameter selection for Bayesian reconstruction of attenuation maps

IEEE Transactions on Nuclear Science ◽

10.1109/23.873026 ◽

2000 ◽

Vol 47 (4) ◽

pp. 1625-1633 ◽

Author(s):

V.Y. Panin ◽

G.L. Zeng ◽

G.T. Gullberg

Keyword(s):

Regularization Parameter ◽

Parameter Selection ◽

Bayesian Reconstruction ◽

Selection For ◽

Regularization Parameter Selection

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Spectral Response Based Regularization Parameter Selection for Total Variation Image Restoration

Lecture Notes in Electrical Engineering - Advanced Multimedia and Ubiquitous Engineering ◽

10.1007/978-981-10-5041-1_101 ◽

2017 ◽

pp. 640-645

Author(s):

Yuhui Zheng ◽

Min Li ◽

Kai Ma ◽

Shunfeng Wang ◽

Jin Wang

Keyword(s):

Image Restoration ◽

Total Variation ◽

Regularization Parameter ◽

Spectral Response ◽

Parameter Selection ◽

Selection For ◽

Regularization Parameter Selection

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Monte Carlo SURE-based regularization parameter selection for penalized-likelihood image reconstruction

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2014.6868065 ◽

2014 ◽

Author(s):

Jian Zhou ◽

Jinyi Qi

Keyword(s):

Monte Carlo ◽

Image Reconstruction ◽

Regularization Parameter ◽

Penalized Likelihood ◽

Parameter Selection ◽

Likelihood Image ◽

Selection For ◽

Regularization Parameter Selection

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