A New Approach for Wavelet Denoising Based on Training

Author

Wang, Zelong ; Yan, Fengxia ; Liu, Jiying ; Zhu, Jubo

Author_Institution

Sch. of Sci., Nat. Univ. of Defense Technol., Changsha, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

4

Abstract

A new approach for wavelet denoising based on training is proposed in this paper. Firstly, the same two images with different noise levels are trained for the parameter of SLT (Slicing the Transform). Secondly, SLT is used to remove the noise iteratively. The benefit of conventional wavelet denoising, such as multi-analysis, is reserved by this paper. Furthermore, the difficulty of selection about natural images for training is avoided and the solidity of the algorithm is enhanced as well as the speed. Experimental results show that the proposed method is effective to a wide range of images; when compared to the classical method, the reconstruct images with our proposed method are with better PSNR (peak signal-to-noise rate) and visual quality.

Keywords

image denoising; wavelet transforms; natural image; slicing-the-transform; wavelet denoising; Context modeling; Hidden Markov models; Image denoising; Image reconstruction; Iterative algorithms; Noise level; Noise reduction; PSNR; Wavelet coefficients; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5301265

Filename

5301265