全变分耦合图像去噪模型

doi:10.11959/j.issn.1000-436x.2016085

Journal on Communications ›› 2016, Vol. 37 ›› Issue (4): 182-191.doi: 10.11959/j.issn.1000-436x.2016085

• Academic communication • Previous Articles Next Articles

Coupling image denoising model based on total variation

Mei-ling WANG^1,^2,³,Xian-chun ZHOU^1,^2,³,Lin-feng ZHOU^1,^2,³,Lan-fang SHI⁴

¹ School of Electronic and Information Engineering, Nanjing Uni ity of Information Science and Technology, Nanjing 210044, China
² Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
³ Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
⁴ School of Mathematics and Statistics, Nanjing University of I formation Science and Technology, Nanjing 210044, China

Online:2016-04-25 Published:2016-04-26
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The Specialized Research Fund for the Doctoral Program of Higher Education;The Jiangsu Information and Communication Engineer-ing Preponderant Discipline Platform, The Natural Science Foundation of Jiangsu Province;The Jiangsu Qing Lan Project and the Natural Sciences Fundation of the Universities of Jiangsu Province

Abstract

Abstract:

The total variation (TV) model used in image denoising may produce “staircase effect”. A coupling image de-noising model based on total variation was proposed. First, a trend fidelity term based on the change tendency of image gradient was established. The fidelity term could not only remove image noise, but also restrain “staircase effect”. Then, wavelet was used to decompose coefficient in frequency domain, control based on the edge detection ability of Canny algorithm were designed. The control functions control energy spread direction, the advantages of TV model and trend fidelity term are maintained, edge and texture details were protected, and “staircase effect'' was also suppressed. Experiment results show that peak signal to noise ratio (PSNR), structure similarity (SSIM) and visual effects of the nov-el model are much better. Moreover, the running time of the novel model is shorter.

Key words: image denoising, Canny algorithm, trend fidelity term, control function

Mei-ling WANG,Xian-chun ZHOU,Lin-feng ZHOU,Lan-fang SHI. Coupling image denoising model based on total variation[J]. Journal on Communications, 2016, 37(4): 182-191.

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References 25

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模型	指标	TV	NLM	BM3D	Non-iterative PPB	Iterative PPB	WHT	WST	本文方法
Buddha	PSNR	34.132 5	28.045 5	29.803 0	28.384 3	28.551 9	25.974 5	25.610 1	49.980 4
Buddha	SSIM	0.944 0	0.742 0	0.887 6	0.754 4	0.771 5	0.641 5	0.626 5	0.997 9
Lena	PSNR	41.411 9	32.759 6	35.121 4	33.114 1	32.891 0	30.341 4	30.102 4	55.904 8
Lena	SSIM	0.981 5	0.889 1	0.961 6	0.886 5	0.892 4	0.812 1	0.808 8	0.999 1

Coupling image denoising model based on total variation

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