• DocumentCode
    310398
  • Title

    Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms

  • Author

    Beaurepaire, Lionel ; Chehdi, Kacem ; Vozel, Benoît

  • Author_Institution
    ENSSAT, Lannion, France
  • Volume
    4
  • fYear
    1997
  • fDate
    21-24 Apr 1997
  • Firstpage
    2805
  • Abstract
    This paper deals with the problem of identifying the nature of noise and estimating its standard deviation from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. In this study, we focus our attention on three classes of degraded noise images, the first one being degraded by an additive noise, the second one by a multiplicative noise and the latter by an impulsive noise. First, in order to identify the nature of the noise, we propose a new approach consisting of characterizing each class by a parameter obtained from histograms computed on several homogeneous regions of the observed image. The homogeneous regions are obtained by segmenting images. Then, the estimation of the standard deviation is achieved from the analysis of an histogram of local standard deviations computed on each of the homogeneous regions
  • Keywords
    identification; image segmentation; optical noise; parameter estimation; statistical analysis; additive noise; analysis algorithm; degraded noise image; estimation; homogeneous regions; image; impulsive noise; local histograms; multiplicative noise; processing algorithm; segmenting; standard deviation; statistical parameters; Additive noise; Algorithm design and analysis; Degradation; Detectors; Filtering algorithms; Histograms; Image analysis; Image edge detection; Image segmentation; Machine vision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
  • Conference_Location
    Munich
  • ISSN
    1520-6149
  • Print_ISBN
    0-8186-7919-0
  • Type

    conf

  • DOI
    10.1109/ICASSP.1997.595372
  • Filename
    595372