Edge-adaptive image interpolation using constrained least squares

Kazu Mishiba, Taizo Suzuki, Masaaki Ikehara

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

    10 Citations (Scopus)

    Abstract

    Some adaptive image interpolation methods have been proposed to create higher visual quality images than traditional interpolation methods such as bicubic interpolation. These methods, however, often suffer from high computational costs and unnatural texture interpolation. This paper proposes a novel edge-adaptive image interpolation method using an edge-directed smoothness filter. Our approach estimates the enlarged image from the original image based on an observation model. The estimated image is constrained to have many edge-directed smooth pixels which are measured by using the edge-directed smoothness filter introduced in this paper. Simulation results show that the proposal method produces images with higher visual quality, higher PSNRs and faster computational times than the conventional methods.

    Original languageEnglish
    Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
    Pages2837-2840
    Number of pages4
    DOIs
    Publication statusPublished - 2010 Dec 1
    Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
    Duration: 2010 Sep 262010 Sep 29

    Publication series

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

    Other

    Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
    CountryHong Kong
    CityHong Kong
    Period10/9/2610/9/29

    Keywords

    • Adaptive image interpolation
    • Edge-directed smoothness
    • Image processing

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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  • Cite this

    Mishiba, K., Suzuki, T., & Ikehara, M. (2010). Edge-adaptive image interpolation using constrained least squares. In 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings (pp. 2837-2840). [5652113] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2010.5652113