Study of anisotropic diffusion model based on pulse coupled neural network and image entropy

Guo Ye-Cai; Zhou Lin-Feng

doi:10.7498/aps.64.194204

Abstract

In image processing, most of the anisotropic diffusion models based on partial differential equation use gradient information to detect image edge. If the image edge is seriously polluted by noise, these methods would not be able to detect image edge, so the edge features cannot be retained. Pulse coupled neural network (PCNN) has the property that similar input neurons can generate pulse at the same time; this property is used to process the noisy image, and we can get an image entropy sequence. The image entropy sequence which will be used as an edge detecting operator is introduced into the diffusion equation, and this will not only reduce the defects produced when the gradient is used as an edge detecting operator so it is easily affected by the noise, but the area image information can also retain more completely. Then, we will use the rule of minimum cross entropy to search for a minimum threshold, which would satisfy the condition that the information difference between noisy image and denoised image is the minimum. The optimal threshold designed will control diffusion intensity reasonably, and the anisotropic diffusion model based on pulse coupled neural network and image entropy (PCNN-IEAD) can be established. Analysis and simulation results show that the proposed model preserves more image information than the classical ones. It removes the image noise and at the same time protects the edge texture details of the image; the proposed model retains the area image information more completely, the performance indexes can also confirm the superiority of the new model. In addition, the operating time of the proposed model is shorter than that of the classical models, therefore, the proposed model may be the ideal one.

Keywords:

Authors and contacts

1.
Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University 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.
College of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Corresponding author: Guo Ye-Cai, guo-yecai@163.com

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11202106, 61201444), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20123228120005), the Jiangsu Information and Communication Engineering Preponderant Discipline Platform, China, Jiangsu Key Laboratory of Meteorological Observation and Information Processing (Grant Nos. KDXS1204, KDXS1403), the Jiangsu Qing Lan Project and the Natural Sciences Fundation from the Universities of Jiangsu Province of China (Grant No. 13KJB170016).

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Cited By

[1]	Zhang W, Li J J, Yang Y P 2014 Signal Process 103 6
[2]	Chumchob N 2013 IEEE Trans. Image Process 22 4551
[3]	Wu T T, Yang Y F, Pang Z F 2012 Appl. Numer. Math. 62 79
[4]	Wu J, Tang C 2011 IEEE Trans. Image Process 20 2428
[5]	Brito-Loeza C, Chen K 2010 IEEE Trans. Image Process 19 1518
[6]	Wang Z, Huang X, Li Y X, Song X N 2013 Chin. Phys. B 22 010504
[7]	Perona P, Malik J 1990 IEEE Trans. Pattern Anal. Mach. Intell. 12 629
[8]	Rudin L I, Osher S, Fatemi E 1992 Physica D 60 259
[9]	Cheng L Y, Tang C, Yan S 2011 Optics Communications 284 5549
[10]	Liu P, Fang H, Li G Q, Liu Z W 2012 IEEE Geosci. Remote Sens. 9 358
[11]	Niang O, Thioune A, Gueirea M C 2012 IEEE Trans. Image Process 21 3991
[12]	Bumsub H, Dongbo M, Kwanghoon So 2013 IEEE Trans. Image Process 22 1096
[13]	Zhou X C, Wang M L, Zhou L F, Wu Q 2015 Acta Phys. Sin. 64 024205(in Chinese) [周先春, 汪美玲, 周林锋, 吴琴 2015 物理学报 64 024205]
[14]	Zhou X C, Shi L F, Han X L, Mo J Q 2014 Chin. Phys. B 23 090204
[15]	Zhou X C, Shi L F, Mo J Q 2014 Chin. Phys. B 23 040202
[16]	Zhang Y H, Ding Y, Wang L H 2011 Procedia Engineering 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 物理学报 62 099501]
[18]	Zhang K K, Gao X B, Li X L 2012 IEEE Trans. Image Process 21 4544
[19]	Dabov K, Foi A, Katkovnik V, Egiazarian K 2007 IEEE Trans. Image Process 16 2080
[20]	Deledalle C A, Denis L, Tupin F 2009 IEEE Trans. Image Process 18 2661
[21]	Nikpour M, Hassanpour H 2010 IET Image Process 4 452
[22]	Kamilov, Bostan E, Unser M 2012 IEEE Signal Process Lett. 19 187
[23]	Johnson J L, Padgett M L 1999 IEEE Trans. Neural Netw. 10 480
[24]	Ranganath H S, Kuntimad G 1999 IEEE Trans. Neural Netw. 10 615
[25]	Weickert J, Bary H R, Max A V 1998 IEEE Trans. Image Process 7 398
[26]	Canny J 1986 IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 679

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Vol. 64, Issue 19 - 2015-10-05

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