Image restoration subject to a total variation constraint

Total variation has proven to be a valuable concept in connection with the recovery of images featuring piecewise smooth components. So far, however, it has been used exclusively as an objective to be minimized under constraints. In this paper, we propose an alternative formulation in which total va...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 13; no. 9; pp. 1213 - 1222
Main Authors: Combettes, P.L., Pesquet, J.-C.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.09.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Additive noise
Algorithms
Applied sciences
Artificial intelligence
Blocking
Computer Graphics
Computer science; control theory; systems
Computer Simulation
Constraint optimization
Deconvolution
Degradation
Exact sciences and technology
Formulations
Hilbert space
Image denoising
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image restoration
Information filtering
Information Storage and Retrieval - methods
Information, signal and communications theory
Joints
Lagrangian functions
Mathematics
Models, Statistical
Numerical Analysis
Numerical Analysis, Computer-Assisted
Optimization
Pattern Recognition, Automated
Pattern recognition. Digital image processing. Computational geometry
Programming
Reproducibility of Results
Restoration
Sensitivity and Specificity
Signal processing
Signal Processing, Computer-Assisted
Subtraction Technique
Telecommunications and information theory
Deconvolution
Simulation
Image processing
Noise reduction
Image restoration
Convex programming
Optimization
ISSN:1057-7149, 1941-0042
Online Access:Get full text
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Description
Summary:Total variation has proven to be a valuable concept in connection with the recovery of images featuring piecewise smooth components. So far, however, it has been used exclusively as an objective to be minimized under constraints. In this paper, we propose an alternative formulation in which total variation is used as a constraint in a general convex programming framework. This approach places no limitation on the incorporation of additional constraints in the restoration process and the resulting optimization problem can be solved efficiently via block-iterative methods. Image denoising and deconvolution applications are demonstrated.
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2004.832922