• DocumentCode
    1570045
  • Title

    Filtering Image Sequences Corrupted by Mixed Noise using a New Fuzzy Algorithm

  • Author

    Saeidi, M. ; Moradi, Mohammad Hassan ; Sagafi, F.

  • Author_Institution
    Iran Telecommun. Res. Center, Univ. of Technol., Tehran, Iraq
  • fYear
    2006
  • Firstpage
    1797
  • Lastpage
    1800
  • Abstract
    In this paper, we will propose a novel fuzzy method in image sequences filtering. The proposed filter assigns adaptive weights based on exponential membership functions and use averaging filter for attenuating noise. Our proposed algorithm in image sequences filtering is much more better than the previous algorithms, Specially if images are corrupted by mixed noise, our proposed method attenuates noise and preserves edges much more better than the previous methods. Our proposed fuzzy algorithm don´t need estimating motion trajectory because their assigned weights to noisy pixels are adaptive and use the correlation of pixels well enough. The proposed filter could remove mixed noise admissibly without requesting to know Gaussian noise variance or salt & pepper noise density. It is shown experimentally that the proposed filter can preserve image structures and edges under motion while attenuating noise, and thus can be effectively used in image sequences filtering.
  • Keywords
    adaptive filters; fuzzy logic; image denoising; image motion analysis; image sequences; adaptive weight; edge preservation; exponential membership function; fuzzy algorithm; image motion; image sequence filtering; mixed noise corruption; Adaptive filters; Filtering algorithms; Gaussian noise; Image coding; Image processing; Image sequences; Motion estimation; Noise reduction; Pixel; Smoothing methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2006 IEEE International Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1522-4880
  • Print_ISBN
    1-4244-0480-0
  • Type

    conf

  • DOI
    10.1109/ICIP.2006.312637
  • Filename
    4106900