Image denoising using K-SVD and non-local means

Songyuan Tang; Jian Yang

doi:10.1109/IWECA.2014.6845763

Image denoising using K-SVD and non-local means

Songyuan Tang, Jian Yang

School of Optics and Photonics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
Publisher	IEEE Computer Society
Pages	886-889
Number of pages	4
ISBN (Print)	9781479945658
DOIs	https://doi.org/10.1109/IWECA.2014.6845763
Publication status	Published - 2014
Event	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 - Ottawa, ON, Canada Duration: 8 May 2014 → 9 May 2014

Publication series

Name	Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014

Conference

Conference	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
Country/Territory	Canada
City	Ottawa, ON
Period	8/05/14 → 9/05/14

Keywords

K-SVD
NLM
image denoising
sparseness represatation

Access to Document

10.1109/IWECA.2014.6845763

Cite this

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title = "Image denoising using K-SVD and non-local means",

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 = "K-SVD, NLM, image denoising, sparseness represatation",

author = "Songyuan Tang and Jian Yang",

year = "2014",

doi = "10.1109/IWECA.2014.6845763",

language = "English",

isbn = "9781479945658",

series = "Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014",

publisher = "IEEE Computer Society",

pages = "886--889",

booktitle = "Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014",

address = "United States",

note = "2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 ; Conference date: 08-05-2014 Through 09-05-2014",

}

Tang, S & Yang, J 2014, Image denoising using K-SVD and non-local means. in Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014., 6845763, Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014, IEEE Computer Society, pp. 886-889, 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014, Ottawa, ON, Canada, 8/05/14. https://doi.org/10.1109/IWECA.2014.6845763

Image denoising using K-SVD and non-local means. / Tang, Songyuan ; Yang, Jian.
Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014. IEEE Computer Society, 2014. p. 886-889 6845763 (Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Image denoising using K-SVD and non-local means

AU - Tang, Songyuan

AU - Yang, Jian

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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KW - sparseness represatation

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M3 - Conference contribution

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ER -

Image denoising using K-SVD and non-local means

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Keywords

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