• DocumentCode
    2155446
  • Title

    Adaptive reconstruction method of missing textures based on perceptually optimized algorithm

  • Author

    Ogawa, Takahiro ; Haseyama, Miki

  • Author_Institution
    Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
  • fYear
    2011
  • fDate
    22-27 May 2011
  • Firstpage
    1157
  • Lastpage
    1160
  • Abstract
    This paper presents an adaptive reconstruction method of missing textures based on structural similarity (SSIM) index. The proposed method firstly performs SSIM-based selection of the optimal known local textures to adaptively obtain subspaces for reconstructing missing textures. Furthermore, from the selected known textures, the missing texture reconstruction maximizing the SSIM index is performed. In this approach, the non-convex maximization problem is reformulated as a quasi convex problem, and the adaptive reconstruction of the missing textures becomes feasible. Experimental results show impressive improvement of the proposed method over previously reported reconstruction methods.
  • Keywords
    concave programming; image reconstruction; image texture; adaptive reconstruction method; local textures; missing textures; nonconvex maximization problem; perceptually optimized algorithm; quasiconvex problem; structural similarity index; Equations; Image reconstruction; Image restoration; Indexes; Mathematical model; Pixel; Reconstruction algorithms; Image restoration; image quality assessment; image texture analysis; interpolation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
  • Conference_Location
    Prague
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4577-0538-0
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2011.5946614
  • Filename
    5946614