The solution of image restoration problems usually requires the use of regularization strategies. The L-curve criterium is a popular heuristic tool for choosing good regularized solutions, when the data noise norm is not a priori known. In this work, we propose replacing the original image restoration problem with a noise-independent equality constrained one and solving it by an iterative Lagrange method. The sequence of the computed iterates defines a discrete L-shaped curve. By numerical results, we show that good regularized solutions correspond with the corner of this curve.

A Lagrange method based L-curve for image restoration

LANDI, GERMANA
2013

Abstract

The solution of image restoration problems usually requires the use of regularization strategies. The L-curve criterium is a popular heuristic tool for choosing good regularized solutions, when the data noise norm is not a priori known. In this work, we propose replacing the original image restoration problem with a noise-independent equality constrained one and solving it by an iterative Lagrange method. The sequence of the computed iterates defines a discrete L-shaped curve. By numerical results, we show that good regularized solutions correspond with the corner of this curve.
2013
3rd International Workshop on New Computational Methods for Inverse Problems (NCMIP 2013)
012011
012011
G Landi
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/382616
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact