Blind image separation using nonnegative matrix factorization with gibbs smoothing

Rafal Zdunek, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Nonnegative Matrix Factorization (NMF) has already found many applications in image processing and data analysis, including classification, clustering, feature extraction, pattern recognition, and blind image separation. In the paper, we extend the selected NMF algorithms by taking into account local smoothness properties of source images. Our modifications are related with incorporation of the Gibbs prior, which is well-known in many tomographic image reconstruction applications, to a underlying blind image separation model. The numerical results demonstrate the improved performance of the proposed methods in comparison to the standard NMF algorithms.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages519-528
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13 Nov 200716 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
Country/TerritoryJapan
CityKitakyushu
Period13/11/0716/11/07

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