Maximum likelihood blind image restoration via alternating minimization

Abd Krim Seghouane

doi:10.1109/ICIP.2010.5650975

Maximum likelihood blind image restoration via alternating minimization

Abd Krim Seghouane^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

7 Citations (Scopus)

Abstract

A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	3581-3584
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5650975
Publication status	Published - 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

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10.1109/ICIP.2010.5650975

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title = "Maximum likelihood blind image restoration via alternating minimization",

abstract = "A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.",

keywords = "Blind image restoration, Kullback-leibler information",

author = "Seghouane, \{Abd Krim\}",

year = "2010",

doi = "10.1109/ICIP.2010.5650975",

language = "English",

isbn = "9781424479948",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "3581--3584",

booktitle = "2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings",

note = "2010 17th IEEE International Conference on Image Processing, ICIP 2010 ; Conference date: 26-09-2010 Through 29-09-2010",

}

Seghouane, AK 2010, Maximum likelihood blind image restoration via alternating minimization. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5650975, Proceedings - International Conference on Image Processing, ICIP, pp. 3581-3584, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 26/09/10. https://doi.org/10.1109/ICIP.2010.5650975

TY - GEN

T1 - Maximum likelihood blind image restoration via alternating minimization

AU - Seghouane, Abd Krim

PY - 2010

Y1 - 2010

N2 - A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.

AB - A new algorithm for Maximum likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.

KW - Blind image restoration

KW - Kullback-leibler information

UR - https://www.scopus.com/pages/publications/78651061156

U2 - 10.1109/ICIP.2010.5650975

DO - 10.1109/ICIP.2010.5650975

M3 - Conference Paper

SN - 9781424479948

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 3581

EP - 3584

BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010

Y2 - 26 September 2010 through 29 September 2010

ER -

Maximum likelihood blind image restoration via alternating minimization

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