A simplified SVR method for blind image denoising

Author

Wu, HanQing ; Sheng, Xia You

Author_Institution

Coll. of Math. & Comput. Sci., Fuzhou Univ., Fuzhou, China

Volume

1

fYear

2011

fDate

15-17 Oct. 2011

Firstpage

47

Lastpage

51

Abstract

Many practical applications need to eliminate noise from noisy images. This paper first introduce a simplified support vector regression (SSVR) method for single image denoising under mixture noise environments. The proposed SSVR method has a lower computational complexity than related SVR approaches to image denoising. Furthermore, we propose a novel multiple SVR approach for multi-image denoising by extending the SSVR method. Experiment results show that the proposed SVR-based approach has good performance in fast removing mixture noise.

Keywords

computational complexity; image denoising; regression analysis; support vector machines; blind image denoising; computational complexity; mixture noise environments; multiimage denoising; noisy images; simplified SVR method; simplified support vector regression method; Gaussian noise; Image denoising; Noise measurement; Noise reduction; Support vector machines; Training; SVR approach; blind multi-image denoising; mixture noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing (CISP), 2011 4th International Congress on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-9304-3

Type

conf

DOI

10.1109/CISP.2011.6100009

Filename

6100009