The Equivalence of Half-Quadratic Minimization and the Gradient Linearization Iteration

A popular way to restore images comprising edges is to minimize a cost function combining a quadratic data-fidelity term and an edge-preserving (possibly nonconvex) regularization term. Mainly because of the latter term, the calculation of the solution is slow and cumbersome. Half-quadratic (HQ) min...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 16; no. 6; pp. 1623 - 1627
Main Authors:	Nikolova, M., Chan, R.H.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.06.2007 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Computer Simulation Cost function Detection, estimation, filtering, equalization, prediction Equivalence Exact sciences and technology Gradient linearization half-quadratic (HQ) regularization Image converters Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image reconstruction Image restoration Information, signal and communications theory Inverse problems Iterative methods Linear Models Mathematical models Mathematics Minimization Minimization methods Noise reduction Numerical Analysis, Computer-Assisted Optimization Optimization methods Regularization Signal and communications theory signal and image restoration Signal processing Signal Processing, Computer-Assisted Signal, noise Studies Telecommunications and information theory Tomography variational methods Cost minimization Variational methods Image processing Quasi Newton method Iterative method Image restoration Gradient linearization Optimization Image reconstruction Inverse problem signal and image restoration Signal restoration inverse problems Cost function Gradient method half-quadratic (HQ) regularization Quadratic function Linearization
ISSN:	1057-7149, 1941-0042
Online Access:	Get full text
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