Separable Markov Random Field Model and Its Applications in Low Level Vision

    Authors

    J. Sun;M. F. Tappen

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Image Process.

    Keywords

    Discriminative learning; image demosaicing; image denoising; Markov; random field (MRF); separable filter; IMAGE; SPARSE; DOMAIN; FILTER; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    This brief proposes a continuously-valued Markov random field (MRF) model with separable filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF modeling. In this framework, we design a novel gradient-based discriminative learning method to learn the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and fast image demosaicing with high-quality results.

    Journal Title

    Ieee Transactions on Image Processing

    Volume

    22

    Issue/Number

    1

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    402

    Last Page

    408

    WOS Identifier

    WOS:000312892000032

    ISSN

    1057-7149

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