• DocumentCode
    1134650
  • Title

    A Semicausal Model for Recursive Filtering of Two-Dimensional Images

  • Author

    Jain, Anil K.

  • Author_Institution
    Department of Electrical Engineering, State University of New York
  • Issue
    4
  • fYear
    1977
  • fDate
    4/1/1977 12:00:00 AM
  • Firstpage
    343
  • Lastpage
    350
  • Abstract
    A two-dimensional discrete stochastic model for representing images is developed. This representation has lower mean square error, compared to a standard autoregressive Markov representation. Application of the model to linear filtering of images degraded by white noise leads to scalar recursive filtering equations requiring only 0(N2log2N) computations for N x N images. The filter algorithm is a hybrid algorithm where the image is transformed along one dimension and spatially filtered, recursively, in the other. Examples on a 255 X 255 image are given.
  • Keywords
    Image processing, Kalman filtering, recursive filtering, two-dimensional filtering, image modelling.; Degradation; Equations; Filtering; Maximum likelihood detection; Mean square error methods; Nonlinear filters; Optical distortion; Statistics; Stochastic processes; Wiener filter; Image processing, Kalman filtering, recursive filtering, two-dimensional filtering, image modelling.;
  • fLanguage
    English
  • Journal_Title
    Computers, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9340
  • Type

    jour

  • DOI
    10.1109/TC.1977.1674844
  • Filename
    1674844
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1134650