Neighborhood issue in single-frame image Super-Resolution

Kevin Su, Qi Tian, Qing Xue, Nicu Sebe, Jingsheng Ma

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

    71 Citations (Scopus)


    Super-Resolution is the problem of generating one or a set of high resolution images from one or a sequence of low-resolution frames. Most methods have been proposed for super-resolution based on multiple low resolution images of the same scene, which is called multiple-frame super-resolution. Only a few approaches produce a high-resolution image from a single low-resolution image, with the help of one or a set of training images from scenes of the same or different types. It is referred to as single-frame super-resolution. This article reviews a variety of single-frame Super-Resolution methods proposed in the recent years. In the paper, a new manifold learning method: locally linear embedding (LLE) and its relation with single-frame superresolution is introduced. Detailed study of a critical issue: "Neighborhood Issue" is presented with related experimental results and analysis. And possible future research is given. © 2005 IEEE.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Multimedia and Expo, ICME 2005
    Number of pages4
    Publication statusPublished - 2005
    EventIEEE International Conference on Multimedia and Expo - Amsterdam, Netherlands
    Duration: 6 Jul 20058 Jul 2005


    ConferenceIEEE International Conference on Multimedia and Expo
    Abbreviated titleICME 2005


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