Majorization–minimization generalized Krylov subspace methods for ℓp–ℓq optimization applied to image restoration

A new majorization–minimization framework for ℓ p – ℓ q image restoration is presented. The solution is sought in a generalized Krylov subspace that is build up during the solution process. Proof of convergence to a stationary point of the minimized ℓ p – ℓ q functional is provided for both convex a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	BIT Numerical Mathematics Ročník 57; číslo 2; s. 351 - 378
Hlavní autoři:	Huang, G., Lanza, A., Morigi, S., Reichel, L., Sgallari, F.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.06.2017
Témata:	Computational Mathematics and Numerical Analysis Mathematics Mathematics and Statistics Numeric Computing Majorization–minimization algorithm – Image restoration minimization Generalized Krylov subspace
ISSN:	0006-3835, 1572-9125
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	A new majorization–minimization framework for ℓ p – ℓ q image restoration is presented. The solution is sought in a generalized Krylov subspace that is build up during the solution process. Proof of convergence to a stationary point of the minimized ℓ p – ℓ q functional is provided for both convex and nonconvex problems. Computed examples illustrate that high-quality restorations can be determined with a modest number of iterations and that the storage requirement of the method is not very large. A comparison with related methods shows the competitiveness of the method proposed.
ISSN:	0006-3835 1572-9125
DOI:	10.1007/s10543-016-0643-8