Image denoising model based on the improved Demons algorithm

Zhou Xian-Chun; Wang Mei-Ling; Zhou Lin-Feng; Wu Qin

doi:10.7498/aps.64.024205

Abstract

According to image registration, we build a new Demons model of image denoising, in which the diffusion access is regarded as image registration. The experimental results indicate that the performance of the model is better than that of the Perona-Malik model: the ill-condition of the model is removed. It is not enough to describe local characteristics only by using the gradient information in the access of image denoising, so a level set curvature which is the driving force of image structure controlling is introduced into the denoising model. Therefore we propose a new model of image denoising based on two driving forces of gradient and curvature. The simulation results show that the two improved models can both suppress noise effectively, their definitions are enhanced obviously, the performance of image denoising model of two driving forces is more greatly improved.

Keywords:

Authors and contacts

1.
College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2.
Jiangsu Technology and Engineering Center for Meteorological Sensor Network, 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
Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11202106, 61302188), the Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20123228120005), the Jiangsu Information and Communication Engineering Preponderant Discipline Platform, China, the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20131005), and the Natural Sciences Fundation from the Universities of Jiangsu Province of China (Grant No. 13KJB170016).

References

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[2]	Liu P, Fang H, Li G Q, Liu Z W 2012 IEEE Geosci. Remote Sens. 9 358
[3]	Bumsub H, Dongbo M, Kwanghoon S 2013 IEEE Trans. Image Process. 22 1096
[4]	Sofou A, Evangelopoulos G, Maragos P 2005 IEEE Geosci. Remote Sens. 3 94
[5]	Li Z B, Liu Z Z, Shi W Z 2014 IEEE Geosci. Remote Sens. 11 743
[6]	Shi L F, Mo J Q 2013 Acta Phys. Sin. 62 040203 (in Chinese) [石兰芳, 莫嘉琪 2013 62 040203]
[7]	Zhou X C, Lin W T, Lin Y H, Mo J Q 2012 Acta Phys. Sin. 61 240202 (in Chinese) [周先春, 林万涛, 林一骅, 莫嘉琪 2012 61 240202]
[8]	Wang Z, Huang X, Li Y X, Song X N 2013 Chin. Phys. B 22 010504
[9]	Wang Z, Huang X, Li Y X, Song X N 2012 Chin. Phys. B 21 050506
[10]	Sun Z G, Han C Z 2010 Acta Phys. Sin. 59 3210 (in Chinese) [孙增国, 韩崇昭 2010 59 3210]
[11]	Sun Z G, Han C Z 2007 Acta Phys. Sin. 56 4565 (in Chinese) [孙增国, 韩崇昭 2007 56 4565]
[12]	Perona P, Malik J 1990 IEEE Trans. Pattern Anal. Mach. Intell. 12 629
[13]	Catte F, Lions P L, Morel J M 1992 SIMA J. Numer. Anal. 29 182
[14]	Li Y Y, Kaveh M 2000 IEEE Trans. Image Process. 9 1723
[15]	Gilboa G, Sochen N, Zeevi Y Y 2002 IEEE Trans. Image Process. 11 689
[16]	Zhang Y H, Ding Y, Wang L H 2011 Proced. Engineer. 15 2778
[17]	Li J C, Ma Z H, Peng Y X, Huang B 2013 Acta Phys. Sin. 62 099501 (in Chinese) [李金才, 马自辉, 彭宇行, 黄斌 2013 62 099501]
[18]	Thirion J P 1988 Med. Image Anal. 2 243
[19]	Wang D K, Hou Y Q, Peng J Y 2008 Partial Differential Equation Method of Image Processing (Beijing: Science Press) pp13-17 (in Chinese) [王大凯,侯愉青,彭进业 2008 图像处理的偏微分方程方法(北京:科学出版社) 第13–17页]
[20]	Weickert J, Bary H R, Max A V 1998 IEEE Trans. Image Process. 7 398

Cited By

[1]	Gonzalez R C, Woods R E (translated by Ruan Q Q, Ruan Y Z) 2010 Digital Image Processing (3rd Edition) (Beijing: Publishing House of Electronics Industry) pp197-213 (in Chinese) [冈萨雷斯R C, 伍兹 R E 著 (阮秋琦, 阮宇智译) 2010(第三版)数字图像处理(北京: 电子工业出版社) 第197–213页]
[2]	Liu P, Fang H, Li G Q, Liu Z W 2012 IEEE Geosci. Remote Sens. 9 358
[3]	Bumsub H, Dongbo M, Kwanghoon S 2013 IEEE Trans. Image Process. 22 1096
[4]	Sofou A, Evangelopoulos G, Maragos P 2005 IEEE Geosci. Remote Sens. 3 94
[5]	Li Z B, Liu Z Z, Shi W Z 2014 IEEE Geosci. Remote Sens. 11 743
[6]	Shi L F, Mo J Q 2013 Acta Phys. Sin. 62 040203 (in Chinese) [石兰芳, 莫嘉琪 2013 62 040203]
[7]	Zhou X C, Lin W T, Lin Y H, Mo J Q 2012 Acta Phys. Sin. 61 240202 (in Chinese) [周先春, 林万涛, 林一骅, 莫嘉琪 2012 61 240202]
[8]	Wang Z, Huang X, Li Y X, Song X N 2013 Chin. Phys. B 22 010504
[9]	Wang Z, Huang X, Li Y X, Song X N 2012 Chin. Phys. B 21 050506
[10]	Sun Z G, Han C Z 2010 Acta Phys. Sin. 59 3210 (in Chinese) [孙增国, 韩崇昭 2010 59 3210]
[11]	Sun Z G, Han C Z 2007 Acta Phys. Sin. 56 4565 (in Chinese) [孙增国, 韩崇昭 2007 56 4565]
[12]	Perona P, Malik J 1990 IEEE Trans. Pattern Anal. Mach. Intell. 12 629
[13]	Catte F, Lions P L, Morel J M 1992 SIMA J. Numer. Anal. 29 182
[14]	Li Y Y, Kaveh M 2000 IEEE Trans. Image Process. 9 1723
[15]	Gilboa G, Sochen N, Zeevi Y Y 2002 IEEE Trans. Image Process. 11 689
[16]	Zhang Y H, Ding Y, Wang L H 2011 Proced. Engineer. 15 2778
[17]	Li J C, Ma Z H, Peng Y X, Huang B 2013 Acta Phys. Sin. 62 099501 (in Chinese) [李金才, 马自辉, 彭宇行, 黄斌 2013 62 099501]
[18]	Thirion J P 1988 Med. Image Anal. 2 243
[19]	Wang D K, Hou Y Q, Peng J Y 2008 Partial Differential Equation Method of Image Processing (Beijing: Science Press) pp13-17 (in Chinese) [王大凯,侯愉青,彭进业 2008 图像处理的偏微分方程方法(北京:科学出版社) 第13–17页]
[20]	Weickert J, Bary H R, Max A V 1998 IEEE Trans. Image Process. 7 398

Catalog

Vol. 64, Issue 2 - 2015-01-20

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