Random-valued impulse noise removal using non-local search for similar structures and sparse representation

Kengo Tsuda, Takanori Fujisawa, Masaaki Ikehara

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

    Abstract

    In this paper, we introduce a new method to remove random-valued impulse noise in an image. Random-valued impulse noise replaces the pixel value at a random position by a random value. Due to the randomness of the noisy pixel values, it is difficult to detect them by comparison with neighboring pixels, which is used in many conventional methods. Then we improve the recent noise detector which uses a non-local search of similar structure. Next we propose a new noise removal algorithm by sparse representation using DCT basis. Furthermore, the sparse representation can remove impulse noise by using the neighboring similar image patch. This method has much more superior noise removal performance than conventional methods.

    Original languageEnglish
    Title of host publication2018 International Workshop on Advanced Image Technology, IWAIT 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-4
    Number of pages4
    ISBN (Electronic)9781538626153
    DOIs
    Publication statusPublished - 2018 May 30
    Event2018 International Workshop on Advanced Image Technology, IWAIT 2018 - Chiang Mai, Thailand
    Duration: 2018 Jan 72018 Jan 9

    Other

    Other2018 International Workshop on Advanced Image Technology, IWAIT 2018
    CountryThailand
    CityChiang Mai
    Period18/1/718/1/9

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Media Technology

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  • Cite this

    Tsuda, K., Fujisawa, T., & Ikehara, M. (2018). Random-valued impulse noise removal using non-local search for similar structures and sparse representation. In 2018 International Workshop on Advanced Image Technology, IWAIT 2018 (pp. 1-4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWAIT.2018.8369628