• DocumentCode
    3356758
  • Title

    Total subset variation prior

  • Author

    Kumar, Sanjeev ; Nguyen, Truong Q.

  • Author_Institution
    Video Process. Lab., UCSD, La Jolla, CA, USA
  • fYear
    2010
  • fDate
    26-29 Sept. 2010
  • Firstpage
    77
  • Lastpage
    80
  • Abstract
    We propose total subset variation (TSV), a convexity preserving generalization of the total variation (TV) prior, for higher order clique MRF. A proposed differentiable approximation of the TSV prior makes it amenable for use in large images (e.g. 1080p). A convex relaxation of sub-exponential distribution is proposed as a criterion to determine the parameters of the optimization problem resulting from the TSV prior. For the super-resolution application, experiments show reconstruction error improvement with respect to the TV and other methods.
  • Keywords
    convex programming; image denoising; image resolution; convex relaxation; differentiable approximation; higher order clique MRF; large images; optimization problem; reconstruction error improvement; sub-exponential distribution; super-resolution application; total subset variation; Approximation methods; Image reconstruction; Image resolution; Optimization; Pixel; TV; Through-silicon vias; MRF; Super-resolution; Total Subset Variation; Total Variation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2010 17th IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4244-7992-4
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2010.5652889
  • Filename
    5652889