• 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