In this paper, a new approach to the problem of impulsive noise reduction in image is presented. First, an image neighborhood hypergraph representation using a similarity measure is computed. Next, a detection procedure based on hypergraph properties is used to classify hyperedges either as noisy, or clean data. Then we apply a nonlinear filter to noisy detected pixels. The results show that the proposed method outperforms most of the basic algorithms for the reduction of impulsive noise.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Similarity hypergraph representation for impulsive noise reduction


    Contributors:
    Rital, S. (author) / Cherifi, H. (author)


    Publication date :

    2003-01-01


    Size :

    307500 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Similarity Hypergraph Representation for Impulsive Noise Reduction

    Rital, S. / Cherifi, H. / IEEE et al. | British Library Conference Proceedings | 2003


    Fast Similarity Based Impulsive Noise Reduction Technique for Color Images

    Smolka, B. / Lukac, R. / IEEE et al. | British Library Conference Proceedings | 2003



    Impulsive noise removal via sparse representation

    Chen, F. / Ma, G. / Lin, L. et al. | British Library Online Contents | 2013


    Detectors of impulsive noise and new effective filters for impulsive noise reduction [5014-51]

    Aizenberg, I. N. / Astola, J. T. / Bregin, T. et al. | British Library Conference Proceedings | 2003