We propose a two-stage variational model for the additive decomposition of images into piecewise constant, smooth, textured and white noise components. The challenging separation of noise from texture is successfully achieved by including a normalized whiteness constraint in the model, and the selection of the regularization parameters is performed based on a novel multi-parameter cross-correlation principle. The two resulting minimization problems are efficiently solved by means of the alternating directions method of multipliers. Numerical results show the potentiality of the proposed model for the decomposition of textured images corrupted by several kinds of additive white noises.

Girometti L., Huska M., Lanza A., Morigi S. (2023). Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection. Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-31975-4_10].

Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection

Girometti L.;Huska M.;Lanza A.;Morigi S.
2023

Abstract

We propose a two-stage variational model for the additive decomposition of images into piecewise constant, smooth, textured and white noise components. The challenging separation of noise from texture is successfully achieved by including a normalized whiteness constraint in the model, and the selection of the regularization parameters is performed based on a novel multi-parameter cross-correlation principle. The two resulting minimization problems are efficiently solved by means of the alternating directions method of multipliers. Numerical results show the potentiality of the proposed model for the decomposition of textured images corrupted by several kinds of additive white noises.
2023
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
120
133
Girometti L., Huska M., Lanza A., Morigi S. (2023). Quaternary Image Decomposition with Cross-Correlation-Based Multi-parameter Selection. Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-31975-4_10].
Girometti L.; Huska M.; Lanza A.; Morigi S.
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/11585/933914
 Attenzione

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

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact