Image denoising by arithmetic means based on similarity

Yutaka Takagi, Masaaki Ikehara

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

    Abstract

    In this paper, we propose a Non-Local Means algorithm-based denoising method. In conventional NLM, the weighting functions are acquired based on the similarity between target patch and its neighboring patches and then Gaussian-range kernel is calculated based on the similarity. Then, target patch is replaced by weighted means value of neighboring patches. In comparison, our method extracts similar patches by thresholding and only calculates simple arithmetic average. The method does not only outperform the conventional NLM but also implement with less computation. Finally, we compare the proposed and the conventional NLM, and validate the advantage.

    Original languageEnglish
    Title of host publication2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781467372183
    DOIs
    Publication statusPublished - 2016 Apr 26
    Event10th International Conference on Information, Communications and Signal Processing, ICICS 2015 - Singapore, Singapore
    Duration: 2015 Dec 22015 Dec 4

    Other

    Other10th International Conference on Information, Communications and Signal Processing, ICICS 2015
    CountrySingapore
    CitySingapore
    Period15/12/215/12/4

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

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  • Cite this

    Takagi, Y., & Ikehara, M. (2016). Image denoising by arithmetic means based on similarity. In 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015 [7459953] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICS.2015.7459953