• DocumentCode
    3428287
  • Title

    A neural network for deblurring an image

  • Author

    Jubien, Chris M. ; Jernigan, M.E.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada
  • fYear
    1991
  • fDate
    9-10 May 1991
  • Firstpage
    457
  • Abstract
    A neural network architecture for deblurring a blurry scene without prior knowledge of the blur is proposed. Two different training algorithms are described, one a standard neural network training algorithm (employing the least mean squares (LMS) rule) and the second an original algorithm, dubbed algorithm-X. Both were successful for developing inverse blur filters to enhance a blurry picture. Algorithm-X is computationally less complex than the LMS algorithm, and in tests comparing the training times of the two algorithms, algorithm-X was found to be faster
  • Keywords
    filtering and prediction theory; least squares approximations; neural nets; picture processing; LMS algorithm; algorithm-X; blurry scene; image deblurring; inverse blur filters; least mean squares; neural network architecture; training algorithms; training times; Degradation; Digital filters; Filtering; Image processing; Layout; Least squares approximation; Neural networks; Neurons; Nonlinear filters; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Computers and Signal Processing, 1991., IEEE Pacific Rim Conference on
  • Conference_Location
    Victoria, BC
  • Print_ISBN
    0-87942-638-1
  • Type

    conf

  • DOI
    10.1109/PACRIM.1991.160776
  • Filename
    160776