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1550 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Figure 9. Fusion results of pepsi: (a) our method, (b) Gradient, (c) DWT, (d) Curvelet, (e) NSCT and (f)-(j) are partial enlargements of (a)-(e), respectively. results with relatively lower contrast, and the results of the NSCT and especially the DWT methods have heavy ringing artifacts; whereas our proposed method produces a result with the highest contrast and the fewest ringing artifacts. Visually, the advantage of our proposed method is prominent. Table Ⅳ shows the fusion indices of results by using the above five image fusion methods for pepsi. The result of our proposed method has the largest mutual information, Q AB/F , and the universal image quality index, except that, it has lower average gradient and spatial frequency than those of the DWT and the NSCT methods. This is because that, for pepsi, severe ringing artifacts in the results of the DWT (Fig. 9(h)) and the NSCT (Fig. 9(j)) may cause larger values in the average gradient and the spatial frequency. Experimental results demonstrate that, the superiority of the proposed method, in the field of visual quality and objective evaluations, is prominent. This mainly benefits from the global coupling characteristics of the ULPCNNs model. By using the features extracted from the output pulses of the ULPCNNs, the biological activity of the HVS to detailed information of images can be reflected very well. VI. CONCLUSION In this paper, we provide a new image fusion algorithm based on the ULPCNNs in the contourlet domain. Directional contrast is fed into the ULPCNNs to imitate the biological activity of HVS to directional information. Linking range is also determined by the contrast, flexibly making good use of global features of images. Experimental results illuminate that, the CT- ULPCNN method outperforms the other methods in both the visual and the objective fields. ACKNOWLEDGMENT This work was supported by the Fundamental Research Funds for the Central Universities (N110323004) and the Natural Science Foundation of Hebei Province under Grant No.F2012501001. Method TABLE I. FUSION INDICES FOR PEPSI Metrics AG SF MI Q AB/F UIQI CT-ULPCNN 5.6722 13.986 6.7704 0.74015 0.89467 CT-Miao 5.5759 13.923 6.4653 0.73644 0.85769 CT-Zheng 5.4912 13.833 6.2256 0.71153 0.84454 CT-Yang 5.5684 13.933 6.4987 0.73185 0.85492 Method TABLE II. FUSION INDICES FOR REMOTE Metrics AG SF MI Q AB/F UIQI CT-ULPCNN 7.0993 15.362 1.6673 0.56055 0.69729 CT-Miao 6.6244 14.646 1.4599 0.53364 0.64608 CT-Zheng 6.6965 14.526 1.4182 0.49636 0.63166 CT-Yang 7.0883 15.037 1.1027 0.46923 0.50629 Method TABLE III. FUSION INDICES FOR CAMP Metrics AG SF MI Q AB/F UIQI CT-ULPCNN 7.2227 13.506 1.5600 0.46466 0.63175 CT-Miao 6.8137 12.747 1.3814 0.4067 0.56411 CT-Zheng 6.7682 12.529 1.3594 0.38244 0.55494 CT-Yang 7.0183 13.064 1.5026 0.38959 0.51602 TABLE IV. FUSION INDICES FOR PEPSI © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1551 Method Metrics AG SF MI Q AB/F UIQI CT-ULPCNN 5.6722 13.986 6.7704 0.74015 0.89467 Gradient 4.7795 11.987 6.135 0.73947 0.88898 DWT 5.8093 14.173 6.3616 0.72958 0.86539 Curvelet 5.6215 13.977 6.5344 0.73633 0.88186 NSCT 7.7004 18.99 6.7607 0.68791 0.78435 REFERENCES [1] Y. F. Li, X. Y. Feng, and Y. Fan, “Investigation of Shift Dependency Effects on Multiresolution-Based Image Fusion Performance,” Journal of Software., vol. 6, no. 3, pp. 475–482, March 2011. [2] X. B. Jin, J. Bao, and J. J. Du, “Image Enhancement Based on Selective - Retinex Fusion Algorithm,” Journal of Software, vol. 7, no. 6, pp. 1187–1194, June 2012. [3] M. Xu, H. Chen, and P. K. Varshney, “An image fusion approach based on Markov random fields,” IEEE Trans. Geosci. Remote Sens., vol. 49, pp. 5116–5127, December 2011. [4] W. Yao and M. Han, “Improved GIHSA for image fusion based on parameter optimization,” Int. J. Remote Sens., vol. 31, pp. 2717–2728, 2010. [5] Z. Zhang and R. S. Blum, “A categorization of multiscaledecomposition-based image fusion schems with a performance study for a digital camera application,” Proc. IEEE, vol. 87, pp. 1315–1326, August 1999. [6] S. Li, B. Yang, and J. Hu, “Performance comparison of different multi-resolution transforms for image fusion,” Inf. Fusion, vol. 12, pp. 74–84, April 2011. [7] X. Cai and G. Han, “Improved Statistical Image Fusion Method Using a Continuous-Valued Blur Factor,” Opt. Eng., vol. 51, pp. 047004-1–047004-10, 2012. [8] M. Chandana, S. Amutha, and N. Kumar, “A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform,” Int. J. Res. Rev. Comput. Sci., vol. 2, pp. 948– 953, August 2011. [9] B. Zhang, “Study on image fusion based on different fusion rules of wavelet transform,” in 3rd Int. Conf. Adv. Comput. Theory Eng.,Proc., 2010, pp. 649–653. [10] M. N. Do and M. Vetterli, “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation,” IEEE Trans. Image Process., vol. 14, pp. 2091–2106, December 2005. [11] X. Cai and W. Zhao, “Discussion upon Effects of Contourlet Lowpass Filter on Contourlet-Based Image Fusion Algorithms,” Acta Autom. Sin., vol. 35, pp. 258– 266, March 2009. [12] Q. G. Miao and B. S. Wang, “A Novel Image Fusion Method Using Contourlet Transform,” in IEEE Int. Conf. Commun. Circuits Sys., 2006, pp. 548–552. [13] Y. A. Zheng, C. S. Zhu, J. S. Song, and X. H. Zhao, “Fusion of Multi-band SAR Images Based on Contourlet Transform.” in IEEE Int. Conf. on Inf. Acquis., 2006, pp. 420–424. [14] L. Yang, B. L. Guo, and W. Ni, “Multifocus Image Fusion Algorithm Based on Region Statistics in Contourlet Domain,” J. Xi'an Jiaotong Univ., vol. 41, pp. 448–452, April 2007. [15] Z. B. Wang, Y. D. Ma, F. Y. Cheng, and L. Z. Yang, “Review of Pulse-Coupled Neural Networks,” Image Vis. Comput., vol. 28, pp. 5–13, January 2010. [16] X. D. Gu, “A New Approach to Image Authentication using Local Image Icon of Unit-linking PCNN,” in IEEE Int. Conf. Neural. Netw., 2006, pp. 1036–1041. [17] S. Wei, Q. Hong, and M. S. Hou, “Automatic image segmentation based on PCNN with adaptive threshold time constant,” Neurocomput., vol. 74, pp. 1485–1491, April 2011. [18] J. C. Fu, C. C. Chen, J. W. Chai, S. T. C. Wong, and I. C. Li, “Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging,” Comput. Med. Imaging Graph., vol. 34, pp. 308–320, June 2010. [19] D. Agrawal and J. Singhai, “Multifocus image fusion using modified pulse coupled neural network for improved image quality,” IET Image Process., vol. 4, pp. 443–451, December 2010. [20] P. J. Burt and R. J. Kolczynski, “Enhanced image capture through fusion,” in Proc. of 4 th Int. Conf. Comput. Vision, 1993, pp. 173–182. [21] F. Hassainia, M. I. Magana, F. Langevin, and J. P. Kernevez, “Image fusion by an orthogonal wavelet transform and comparison with other methods,” in 14 th Annu. Int. Conf. IEEE, 1992, pp. 1246–1247. [22] H. H. Li, L. Guo, and H. Liu, “Research on image fusion based on the second generation curvelet transform,” Acta Opt. Sin., vol. 26, pp. 657–662, May 2006. [23] X. B. Qu, G. F. Xie, J. W. Yan, Z. Q. Zhu, and B. G. Chen, “Image fusion algorithm based on neighbors and cousins information in nonsubsampled contourlet transform domain,” in Proc. Int. Conf. Wavelet Anal. Pattern Recognit., 2007, pp. 1797−1802. [24] V. Petrovic and C. S. Xydeas, “Objective evaluation of signal-level image fusion performance,” Opt. Eng., vol. 44, pp. 087003-1–087003-8, August 2005. [25] G. Piella and H. Heijmans, “A New Quality Metric for Image Fusion,” in IEEE Int. Conf. Image Process., 2003, pp. 173−176. Xi Cai received her B.S. and Ph.D. degrees from the School of Electronic and Information Engineering, Beihang University, China, in 2005 and 2011, respectively. Now she is a teacher at Engineering Optimization and Smart Antenna Institute, Northeastern University at Qinhuangdao, China. Her research interests include image fusion, image registration and object detection. Guang Han received his B. Eng. and M. Eng. degrees from the School of Electronic and Information Engineering, Beihang University, China, in 2005 and 2008, respectively. Now he is a Ph.D. candidate at College of Information Science and Engineering, Northeastern University. His research interests include object detection and object tracking based on video sequences. Jinkuan Wang received the M.Eng. degree from Northeastern University, Shenyang, China, in 1985, and the Ph.D. degree from the University of Electro-Communications, Chofu, Japan, in 1993. In 1990, he joined the Institute of Space and Astronautical Science, Sagamihara, Japan, as a special member. He was an Engineer with the Research Department, COSEL Company, in 1994. He is currently the President of the Northeastern University at Qinhuangdao, Hebei, China, where he has been a Professor since 1998. He has been a main researcher in several National Natural Science Foundation research projects of China. His main interests are in the areas of intelligent control, adaptive array, wireless sensor networks and image processing. © 2013 ACADEMY PUBLISHER
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Corn Moisture Measurement using a C
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Call for Papers and Special Issues
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