Sparsity based super resolution using color channel constraints

Hojjat S. Mousavi, Vishal Monga

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Scopus citations

    Abstract

    Sparsity constrained single image super-resolution has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then use the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution only use luminance channel information and do not use any information from other color channels. In this work, we extend sparsity based super-resolution to multiple color channels. Edge similarities amongst color bands are exploited as cross channel correlation constraints. These additional constraints lead to a new optimization problem which is not easily solvable; however, a tractable solution is proposed to solve it efficiently. Experimental results shows the merits of our proposed method both visually and quantitatively.

    Original languageEnglish (US)
    Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
    PublisherIEEE Computer Society
    Pages579-583
    Number of pages5
    ISBN (Electronic)9781467399616
    DOIs
    StatePublished - Aug 3 2016
    Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
    Duration: Sep 25 2016Sep 28 2016

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    Volume2016-August
    ISSN (Print)1522-4880

    Other

    Other23rd IEEE International Conference on Image Processing, ICIP 2016
    CountryUnited States
    CityPhoenix
    Period9/25/169/28/16

    All Science Journal Classification (ASJC) codes

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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