Multi-focus image fusion based on the nonsubsampled contourlet transform and dual-layer PCNN model

被引：8

作者：

Xin G. ^{[1
,2
]}

Zou B. ^{[1
]}

Li J. ^{[1
,3
]}

Liang Y. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Central South University, Changsha

[2] Department of Information and Technology, Hunan Radio and TV University, Changsha

[3] School of Mathematics and Computer Science, Jishou University, Jishou

来源：

Information Technology Journal | 2011年 / 10卷 / 06期

关键词：

Dual-layer PCNN model; Focus measurement; Image fusion; Nonsubsampled contourlet transform; Shannon information entropy;

D O I：

10.3923/itj.2011.1138.1149

中图分类号：

学科分类号：

摘要：

Image fusion is an important research field of image processing. How to get the best fusion quality is not an easy problem for the researchers. The nonsubsampled contourlet transform (NSCT) is a multiresolution tool for image fusion. For NSCT, how to get a better focus measurement is an important research content. In this study, a new model named dual-layer PCNN model is proposed. It simulates human visual perception mechanism. The model not only takes into account local neighbor relativity each other but also takes into account the relativity between before and after layers. Compared with the other PCNN models, this model uses the Shannon information entropy to adaptively control its iteration process. Based on this model and NSCT, a new image fusion method is proposed. In the method, the source images are decomposed by NSCT firstly and then the dual-layer PCNN model and local energy match rule are used to select the coefficients. At last, the fused image is reconstructed by taking an inverse NSCT. The experimental results show that the duallayer PCNN model is a good focus measurement for NSCT and the method proposed in this study has better fusion performance than the other classical methods. © 2011 Asian Network for Scientific Information.

引用

页码：1138 / 1149

页数：11

共 21 条

[1]

Burt P.J., A gradient pyramid basis for pattern-selective image fusion, Proc. Soc. Inform. Display Conf, 23, pp. 467-470, (1992)

[2]

Cheng L., Kong L., Li X., Solving information fusion problems on unreliable evidential sources with generalized DSmT, Journal of Applied Sciences, 6, 7, pp. 1581-1585, (2006)

[3]

Choi M., Kim R.Y., Nam M.-R., Kim H.O., Fusion of multispectral and panchromatic satellite images using the curvelet transform, IEEE Geoscience and Remote Sensing Letters, 2, 2, pp. 136-140, (2005)

[4]

Cvejic N., Seppanen T., Godsill S.J., A nonreference image fusion metric based on the regional importance measure, IEEE J. Selected Topics Signal Process., 3, pp. 212-221, (2009)

[5]

Do M.N., Vetterli M., The contourlet transform: An efficient directional multiresolution image representation, IEEE Transactions on Image Processing, 14, 12, pp. 2091-2106, (2005)

[6]

Eckhorn R., Neural mechanisms of scene segmentation: Recordings from the visual cortex suggest basic circuits for linking field models, IEEE Trans. Neural Networks, 10, pp. 464-479, (1999)

[7]

Eckhorn R., Reitboeck H.J., Arndt M., Dicke P., Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex, Neural Comput, 2, pp. 293-307, (1990)

[8]

Li E., Fan Y., Li X., Information fusion technology with application to the localization of pipeline leakage based on negative pressure wave detection, Journal of Applied Sciences, 6, 9, pp. 2010-2013, (2006)

[9]

Kirankumar Y., Devi S.S., Medical image fusion transforms-2D approach, Journal of Medical Sciences, 7, 5, pp. 870-874, (2007)

[10]

Li H., Manjunath B.S., Mitra S.K., Multisensor image fusion using the wavelet transform, Graphic. Models Image Process., 57, pp. 235-245, (1995)

← 1 2 3 →