Foveated Nonlocal Self-Similarity

    Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

    23 Sitaatiot (Scopus)
    30 Lataukset (Pure)

    Abstrakti

    When we gaze a scene, our visual acuity is maximal at the fixation point (imaged by the fovea, the central part of the retina) and decreases rapidly towards the periphery of the visual field. This phenomenon is known as foveation. We investigate the role of foveation in nonlocal image filtering, installing a different form of self-similarity: the foveated self-similarity. We consider the image denoising problem as a simple means of assessing the effectiveness of descriptive models for natural images and we show that, in nonlocal image filtering, the foveated self-similarity is far more effective than the conventional windowed self-similarity. To facilitate the use of foveation in nonlocal imaging algorithms, we develop a general framework for designing foveation operators for patches by means of spatially variant blur. Within this framework, we construct several parametrized families of operators, including anisotropic ones. Strikingly, the foveation operators enabling the best denoising performance are the radial ones, in complete agreement with the orientation preference of the human visual system.

    AlkuperäiskieliEnglanti
    Sivut78–110
    Sivumäärä33
    JulkaisuInternational Journal of Computer Vision
    Vuosikerta120
    Numero1
    Varhainen verkossa julkaisun päivämäärä9 maalisk. 2016
    DOI - pysyväislinkit
    TilaJulkaistu - 2016
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

    Julkaisufoorumi-taso

    • Jufo-taso 3

    !!ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition

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