DocumentCode :
2271772
Title :
Gaussian Noise Removal of Image on the Local Feature
Author :
He, Kun ; Luan, Xin-Cheng ; Li, Chun-Hua ; Liu, Ran
Author_Institution :
Comput. Coll., Sichuan Univ., Chengdu
Volume :
3
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
867
Lastpage :
871
Abstract :
The traditional removing algorithm of Gaussian noise can only reduce the effect of noise rather than remove it. Furthermore, the noise points in the image will diffuse after removing. According to the effect of the Gaussian noise on the visual images, this paper introduces an algorithm based on the local feature of the image to eliminate Gaussian noise, and this method overcomes the defects of traditional methods. Firstly, we categorize the location of the pixels into three classes-on the noise point, on the edge, and in the local texture, based on the local continuous smoothing in the image. Secondly, we can extract the edge information and texture of the image by morphology according to the local continuity of the image edge and texture property, then we can accurately locate the noise points of the image. Lastly, we use adaptive neighborhood to eliminate the other noise points. Comparing to the traditional methods, this algorithm can remove the noise better and have satisfying image visual impression.
Keywords :
Gaussian noise; adaptive filters; edge detection; feature extraction; image denoising; image texture; smoothing methods; Gaussian noise removal; adaptive filtering; image edge location; image visual impression; local continuous image smoothing; local feature; local image texture; noise point location; pixel location; visual images; Filters; Frequency; Gaussian distribution; Gaussian noise; Histograms; Morphology; Noise level; Noise reduction; Pixel; Smoothing methods; Gaussian Noise; Local neighborhood feature; the edge and texture location;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
Type :
conf
DOI :
10.1109/IITA.2008.552
Filename :
4740121
Link To Document :
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