DocumentCode :
3447869
Title :
Oil film denoising method based on jump regression analysis
Author :
Hua-jun Song ; Peng Ren ; Wei-fang Liu
Author_Institution :
Coll. of Inf. & Control Eng., China Univ. of Pet.(East China), Dongying, China
fYear :
2012
fDate :
16-18 Oct. 2012
Firstpage :
326
Lastpage :
329
Abstract :
The key process of Ship-borne/shore-based radar image oil film recognition method is image denoising. In order to overcome the disadvantage of image edge blur produced by traditional denoising method, the Jump Regression Analysis(JRA) is used to remove radar image noise. Moreover, an improved JRA algorithm is also proposed which improves the denoising effect and reduces the run time of the JAR. It is proved by experiment that the proposed method not only can effectively remove the noise of radar image, but also to maintain the oil film edge information.
Keywords :
edge detection; image denoising; image recognition; image restoration; oil pollution; radar imaging; regression analysis; ships; JAR run time reduction; image edge blurring; jump regression analysis; oil film denoising effect improvement; oil film edge information; radar image noise removal; ship-borne/shore-based radar image oil film recognition method; Films; Image edge detection; Noise; Noise reduction; Radar imaging; Synthetic aperture radar; JRA; Radar imaging; image denoising; oil recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-0965-3
Type :
conf
DOI :
10.1109/CISP.2012.6469934
Filename :
6469934
Link To Document :
بازگشت