Accelerating multi-scale image fusion algorithms using CUDA

Seung Hun Yoo, Jin Hyung Park, Chang Sung Jeong

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

    9 Citations (Scopus)

    Abstract

    Recently, fusion speed has emerged as an important factor in the image fusion and a substantial amount of memory and computing power are required for a high-speed fusion. This paper shows approaches to accelerate multi-scale image fusion speed on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). The GPU has evolved into a very powerful and flexible streaming processor, which provides a good computational power and memory bandwidth. We implement the multi-scale image fusion algorithms using CUDA software platform of the latest version of GPU and theirs fusion speeds are compared and evaluated with implementation in Core2 Quad processor with 2.4GHz. The GPU version achieved a speedup of 6x-8x over the CPU version.

    Original languageEnglish
    Title of host publicationSoCPaR 2009 - Soft Computing and Pattern Recognition
    Pages278-282
    Number of pages5
    DOIs
    Publication statusPublished - 2009
    EventInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca, Malaysia
    Duration: 2009 Dec 42009 Dec 7

    Publication series

    NameSoCPaR 2009 - Soft Computing and Pattern Recognition

    Other

    OtherInternational Conference on Soft Computing and Pattern Recognition, SoCPaR 2009
    Country/TerritoryMalaysia
    CityMalacca
    Period09/12/409/12/7

    Keywords

    • CUDA
    • GPU
    • Image fusion
    • Pyramid
    • Wavelet

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Vision and Pattern Recognition
    • Software

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