Variational Bayesian Image Restoration With a Product of Spatially Weighted Total Variation Image Priors

In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian infe...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 19; no. 2; pp. 351 - 362
Main Authors: Chantas, G., Galatsanos, N.P., Molina, R., Katsaggelos, A.K.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Approximation
Bayesian analysis
Bayesian methods
Exact sciences and technology
Flexibility
History
Image processing
Image restoration
Inference algorithms
Information, signal and communications theory
Inverse problems
Lagrangian functions
No Keywords
Noise robustness
Parameter estimation
Restoration
Signal processing
Telecommunications and information theory
Television
Bayes estimation
State of the art
Image processing
Television
Image restoration
Algorithm
Flexibility
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:In this paper, a new image prior is introduced and used in image restoration. This prior is based on products of spatially weighted total variations (TV). These spatial weights provide this prior with the flexibility to better capture local image features than previous TV based priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed restoration algorithm is fully automatic in the sense that all necessary parameters are estimated from the data and is faster than previous similar algorithms. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2009.2033398