• DocumentCode
    2426813
  • Title

    Advanced Image Processing Techniques for Maximum Information Recovery

  • Author

    Luo, Jiecai ; Cross, James

  • Author_Institution
    Dept. of Electr. Eng., Southern Univ., Baton Rouge, LA
  • fYear
    2007
  • fDate
    4-6 March 2007
  • Firstpage
    58
  • Lastpage
    62
  • Abstract
    Some radio frequency and optical sensors collect large-scale sets of spatial imagery data whose content is often obscured by fog, clouds, foliage and other intervening structures. Often, the obstruction is such as to render unreliable the definition of underling images. There are several typical mathematical methods used in image processing to remove interferences from images to include spectral methods, wave front or shock methods, and the use of non-abelian group operations. In this paper, a new advanced image processing technique based on image segmentations has been developed and tested for the removal of fog, clouds, foliage and other interfering structures. The developed method has been applied to certain images to demonstrate its effectiveness in removing unwanted sub-images.
  • Keywords
    blind source separation; image segmentation; pattern clustering; K-means clustering; cloud removal; fog removal; foliage removal; image processing techniques; image segmentation; information recovery; interference removal; optical sensors; radio frequency sensors; spatial imagery data; Clouds; Electric shock; Image processing; Image segmentation; Interference; Large-scale systems; Optical sensors; Radio frequency; Rendering (computer graphics); Testing; Image segmentation; K-means clustering; Nonlinear processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 2007. SSST '07. Thirty-Ninth Southeastern Symposium on
  • Conference_Location
    Macon, GA
  • ISSN
    0094-2898
  • Print_ISBN
    1-4244-1126-2
  • Electronic_ISBN
    0094-2898
  • Type

    conf

  • DOI
    10.1109/SSST.2007.352317
  • Filename
    4160803