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
Link To Document :
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