Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery

We address the minimization of regularized convex cost functions which are customarily used for edge-preserving restoration and reconstruction of signals and images. In order to accelerate computation, the multiplicative and the additive half-quadratic reformulation of the original cost-function hav...

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Vydáno v:	SIAM journal on scientific computing Ročník 27; číslo 3; s. 937 - 966
Hlavní autoři:	Nikolova, Mila, Ng, Michael K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2005
Témata:	Applied mathematics Calculus of variations and optimal control Exact sciences and technology Inverse problems Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical linear algebra Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Partial differential equations Sciences and techniques of general use Variational methods Optimization method optimization A posteriori estimation inverse problems Convergence rate 65K10 Ill posed problem Signal reconstruction Quadratic form 94A12 49N45 Mathematical programming 35A15 Convergence acceleration Numerical linear algebra half-quadratic regularization 65Y20 Image restoration Inverse problem Half quadratic regularization Regularization method Upper bound signal and image restoration Numerical analysis Scientific computation convergence analysis 65F22 94A08 maximum a posteriori estimation Preconditioning
ISSN:	1064-8275, 1095-7197
On-line přístup:	Získat plný text
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