DocumentCode
3559323
Title
Decorrelating the Structure and Texture Components of a Variational Decomposition Model
Author
Shahidi, Reza ; Moloney, Cecilia
Author_Institution
Fac. of Eng. & Appl. Sci., Memorial Univ. of Newfoundland, St. John´´s, NL
Volume
18
Issue
2
fYear
2009
Firstpage
299
Lastpage
309
Abstract
The observation has been made by Aujol and Gilboa that the cartoon and texture components of the decomposition of an image should not be correlated, as they are generated from independent processes. They use this observation in order to choose an optimal fidelity parameter lambda for the decomposition process. However, this determination can be quite inefficient since a wide range of parameters lambda must be searched through before an estimated optimal parameter can be found. In the present paper, we take a different approach, in which the cartoon and texture components are explicitly decorrelated by adding a decorrelation term to the energy functional of the decomposition model of Osher, Sole, and Vese (the OSV model). Decomposition results of improved quality over those from the OSV model are obtained, as quantified by a series of new decomposition quality measures, with cartoon and texture information better separated into their respective components. A new derivation of the OSV model is developed which maintains the texture subcomponents g1 and g2 so that discrimination results similar to those from other decomposition models (e.g., from the model of Vese and Osher and Improved Edge Segregation) may be obtained. This derivation is extended to the proposed model, for which discrimination results are obtained in a substantially smaller number of iterations.
Keywords
decorrelation; image texture; OSV model; Osher-Sole-Vese decomposition model; cartoon component; image decomposition; optimal parameter estimation; texture component decorrelation; variational decomposition model; Correlation coefficient; image decomposition; texture discrimination; variational methods; Algorithms; Artificial Intelligence; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic;
fLanguage
English
Journal_Title
Image Processing, IEEE Transactions on
Publisher
ieee
Conference_Location
12/9/2008 12:00:00 AM
ISSN
1057-7149
Type
jour
DOI
10.1109/TIP.2008.2008046
Filename
4703202
Link To Document