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

光电学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

7 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
出版商	IEEE Computer Society
页	886-889
页数	4
ISBN（印刷版）	9781479945658
DOI	https://doi.org/10.1109/IWECA.2014.6845763
出版状态	已出版 - 2014
活动	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014 - Ottawa, ON, 加拿大期限: 8 5月 2014 → 9 5月 2014

出版系列

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

会议

会议	2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014
国家/地区	加拿大
市	Ottawa, ON
时期	8/05/14 → 9/05/14

访问文件

10.1109/IWECA.2014.6845763

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{a72142868f604435abc1dd0ad97bd659,

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. 在 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, 页码 886-889, 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014, Ottawa, ON, 加拿大, 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. 页码 886-889 6845763 (Proceedings - 2014 IEEE Workshop on Electronics, Computer and Applications, IWECA 2014).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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.

KW - K-SVD

KW - NLM

KW - image denoising

KW - sparseness represatation

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M3 - Conference contribution

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

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ER -

Image denoising using K-SVD and non-local means

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