• DocumentCode
    1228944
  • Title

    Adaptive schemes for noise filtering and edge detection by use of local statistics

  • Author

    Sun, X.Z. ; Venetsanopoulos, Anastasios N.

  • Author_Institution
    Dept. of Electr. Eng., Changsha Railway Inst., Hunan, China
  • Volume
    35
  • Issue
    1
  • fYear
    1988
  • fDate
    1/1/1988 12:00:00 AM
  • Firstpage
    57
  • Lastpage
    69
  • Abstract
    Some adaptive schemes for noise filterings and edge detection of digital signals are developed. They are bases on the minimum-mean-square-error estimate of the information-bearing signal corrupted by additive noise. The estimate is computed using the local statistics of the input signal and noise. The output is fed back to the input, and the difference between the input and the output is used as the noise estimator. The local statistics of signal and noise are computed through a moving signal window and a moving noise window, which are over the input signal and the noise estimator, respectively. These schemes change their performance according to the local signal-to-noise ratio adaptively. Two kinds of adaptive filtering algorithms and an edge detection algorithm are considered. Their performance in the presence of noise is evaluated and compared to the performance of some other methods. Simulation results on one-dimensional signals and real images are presented
  • Keywords
    filtering and prediction theory; noise; picture processing; signal processing; adaptive filtering algorithms; adaptive schemes; digital signals; edge detection; local statistics; minimum-mean-square-error estimate; moving noise window; moving signal window; noise estimator; noise filtering; one-dimensional signals; real images; Adaptive filters; Arithmetic; Digital filters; Filtering; Finite impulse response filter; Image edge detection; Nonlinear filters; Signal processing algorithms; Signal to noise ratio; Statistics;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0098-4094
  • Type

    jour

  • DOI
    10.1109/31.1700
  • Filename
    1700