Image Denoising Using Wavelet and Support Vector Regression

  • Authors:

  • Hui Cheng;Qiuze Yu;Jinwen Tian;Jian Liu

  • Affiliations:

  • Huazhong University of Science and Technology;Huazhong University of Science and Technology;Huazhong University of Science and Technology;Huazhong University of Science and Technology

  • Venue:
  • ICIG '04 Proceedings of the Third International Conference on Image and Graphics
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

Wavelet image denoising has been well acknowledged as an important method of denoising in image processing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising technique with support vector regression (SVR). Based on the least squares support vector machine (LS-SVM), a new denoising operators used in the wavelet domain are obtained. Simulated noise images are used to evaluate the denoising performance of proposed algorithm along with another wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the prevented edge information in most cases. It also achieves better performance than the median filter.