Wu Yan, Liu Chong-yang, Liao Gui-sheng . A New Pixel-Level Multi-focus Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2800-2804. doi: 10.3724/SP.J.1146.2006.00667
Citation:
Wu Yan, Liu Chong-yang, Liao Gui-sheng . A New Pixel-Level Multi-focus Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2800-2804. doi: 10.3724/SP.J.1146.2006.00667
Wu Yan, Liu Chong-yang, Liao Gui-sheng . A New Pixel-Level Multi-focus Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2800-2804. doi: 10.3724/SP.J.1146.2006.00667
Citation:
Wu Yan, Liu Chong-yang, Liao Gui-sheng . A New Pixel-Level Multi-focus Image Fusion Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(12): 2800-2804. doi: 10.3724/SP.J.1146.2006.00667
A new fusion method for fusing two spatially registered multi-focus images is proposed in this paper. It is based on multi-resolution wavelet decomposition, Self-Organizing Feature Map (SOFM) neural networks and Evolutionary Strategies (ES). First, a normalized feature image, which represents the local region clarity difference of two source images, is extracted by redundant wavelet transform, then the feature image is clustered by SOFM learning algorithm and every pixel pair in source images is classified into a certain class which indicates different clarity differences. Finally, to each pixel pair in different classes, different fusion factors are used to fuse it; these fusion factors are determined by evolution strategies to achieve the best fusion performance. Experimental results show that the proposed method outperforms the Laplace transform and wavelet transform methods.
[1] Genderen J L and Van. Pohl C. Image fusion: Issues, techniques and applications, Intelligent Image Fusion. Proceedings EARSeL Workshop, Strasbourg, France, 11 Sept, 1994: 18-26. [2] Hall D L. Mathematical Techniques in Multisensor Data Fusion. Boston, Artech House, 1992: 20-59. [3] Burt P J and Adelson E H. The Laplacian pyramid as a compact image code [J].IEEE Trans. on Commun.1983, 31(4):532-540 [4] Burt P T and Lolczynski R J. Enhanced image capture through fusion. IEEE Proceedings of the 4th international Conference On Computer Vision, Berlin, Germany, 1993: 173-182. [5] Toet A. Hierarchical image fusion[J].Machine Vision and Application.1990, 3(2):1-11 [6] Li H, Manjunath B S, and Mitra S K. Multisensor image fusion using the wavelet transform[J].Graphical Models and Image Processing.1995, 57(3):235-245 [7] David A Y. Image merging and data fusion by means of the discrete two-dimensional wavelet transform[J].J.Opt. Soc. Am. A.1995, 12(9):1834-1841 [8] Zhang Z and Blum R S. A categorization of multiscale decomposition-based image fusion schemes with a performance study for a digital camera application[J].Proc. IEEE.1999, 87(8):1315-1326 [9] Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation[J].IEEE Trans. on Pattern Analysis and Machina Intelligence.1989, 11(7):674-693 [10] Kohonen T. Self-organized formation of topologically correct feature maps[J].Biological Cybernetics.1982, 43(1):59-69 [11] Fogel D B and Fogel L J. An introduction to simulated evolutionary optimization[J]. IEEE Trans. on NN, 1994, 5(1): 3-14. [12] Li Shutao, Kwok J T, and Wang Yaonan. Multi-focus image fusion using artificial neural networks[J].Pattern Recognition Letters.2002, 23(6):985-997