Image denoising using K-SVD and non-local means

Author

Songyuan Tang ; Jian Yang

Author_Institution

Beijing Eng. Res. Center of Mixed Reality & Adv. Display, Beijing Inst. of Technol., Beijing, China

fYear

2014

fDate

8-9 May 2014

Firstpage

886

Lastpage

889

Abstract

This paper proposes an image denoising method, which exploit the non-local mean (NLM) algorithm and the sparse representation of images. The sparseness is computed by K-SVD and combined with the non-local mean algorithm. Images (Lena, House, Peppers, and Barbaba) with various noise levels (sigma =10, 20, 30, 40, and 50) are used to test the proposed method. The experimental results show that the NLM algorithm only performs better at the low noise level, while the proposed method performs better within a large range noise levels. The PSNR´s means of total images and all noises are 27.1712 and 27.7262 for the NLM and the proposed method. PSNR of the proposed method is 2% more than that of NLM algorithm. This indicates the proposed method performs better.

Keywords

image denoising; image representation; singular value decomposition; K-SVD; NLM algorithm; PSNR; image denoising method; noise levels; nonlocal mean algorithm; sparse image representation; Filtering; Filtering algorithms; PSNR; K-SVD; NLM; image denoising; sparseness represatation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Computer and Applications, 2014 IEEE Workshop on

Conference_Location

Ottawa, ON

Type

conf

DOI

10.1109/IWECA.2014.6845763

Filename

6845763