Perceptually Optimized Missing Texture Reconstruction via Neighboring Embedding

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Author(s)

    • OGAWA Takahiro
    • Graduate School of InformationScience and Technology, Hokkaido University
    • HASEYAMA Miki
    • Graduate School of InformationScience and Technology, Hokkaido University

Abstract

Perceptually optimized missing texture reconstruction via neighboring embedding (NE) is presented in this paper. The proposed method adopts the structural similarity (SSIM) index as a measure for representing texture reconstruction performance of missing areas. This provides a solution to the problem of previously reported methods not being able to perform perceptually optimized reconstruction. Furthermore, in the proposed method, a new scheme for selection of the known nearest neighbor patches for reconstruction of target patches including missing areas is introduced. Specifically, by monitoring the SSIM index observed by the proposed NE-based reconstruction algorithm, selection of known patches optimal for the reconstruction becomes feasible even if target patches include missing pixels. The above novel approaches enable successful reconstruction of missing areas. Experimental results show improvement of the proposed method over previously reported methods.

Journal

  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A(8), 1709-1717, 2015

    Institute of Electronics, Information and Communication Engineers

Codes

  • NII Article ID (NAID)
    130005089900
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    0916-8508
  • Data Source
    IR  J-STAGE 
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