吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (01): 225-234.doi: 10.13229/j.cnki.jdxbgxb201401037
高印寒1, 陈广秋2,3, 刘妍妍2,3
GAO Yin-han1, CHEN Guang-qiu2,3, LIU Yan-yan2,3
摘要:
为了提升多源图像融合精度,提出了一种基于图像质量评价参数的非下采样剪切波(NSST)域图像自适应融合方法。利用非下采样剪切波变换对源图像进行多尺度、多方向分解,低频子带图像采用结构相似度与空间频率两种图像评价参数作为系数权值,高频子带图像应用绝对值与邻域平均能量一致性选择的融合策略。应用非下采样剪切波逆变换重构图像。采用多组多源图像进行融合实验,并对融合结果进行了客观评价。实验结果表明:本文方法在主观和客观评价上均优于其他多尺度融合方法,具有更好的融合效果。
中图分类号:
[1] 胡钢, 刘哲, 徐小平, 等. 像素级图像融合技术的研究与进展[J].计算机应用研究, 2008, 25(3):650-655. Hu Gang, Liu Zhe, Xu Xiao-ping, et al. Research and recent development of image fusion at pixel level[J].Application Research of Computers, 2008, 25(3):650-655.[2] Cheng Ying-lei, Zhao Rong-chun, Wang Bing, et al. An optimal algorithm of multisensor image fusion based on wavelet transform[C]//Processings of 7th International Conference on Signal Processing, Xi'an, 2004:1049-1051.[3] Li Ming, Wu Shun-jun. A new image fusion algorithm based on wavelet transform[C]//Proceedings of 5th International Conference on Computational Intelligence and Multimedia Applications, Xi'an, 2003: 154-159.[4] Candès E J, Donoho D L. New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities[J]. Communications on Pure and Applied Mathematics, 2004, 57(2):219-266.[5] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14 (12):2091-2106.[6] Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform[J].Applied and Computational Harmonic Analysis, 2008, 25(1):25-46.[7] 叶传奇, 苗启广, 王保树. 基于非子采样的Contourlet变换的图像融合方法[J]. 计算机辅助设计与图形学学报, 2007, 19(10):1274-1278. Ye Chuan-qi, Miao Qi-guang, Wang Bao-shu. Image fusion method based on the nonsubsampled contourlet transform[J]. Journal of Computer-Aided Design & Computer Graphics, 2007, 19(10):1274-1278.[8] Zheng You-zhi, Hou Xiao-dong, Bian Tian-tian, et al. Effective image fusion rules of multiscale image decomposition[C]//Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis, Istanbul, 2007:362-366.[9] 张强, 郭宝龙. 基于Curvelet变换的图像融合算法[J].吉林大学学报:工学版, 2007, 37(2):458-463. Zhang Qiang, Guo Bao-long. Image fusion algorithm using Curvelet transform[J].Journal of Jilin University (Engineering and Technology Edition), 2007, 37(2):458-463.[10] Guo K, Labate D, Lim W Q, et al. Wavelets with composite dilations and their MRA properties[J]. Applied and Computational Harmonic Analysis, 2006, 20(2):202-236.[11] Guo K, Labate D. Optimally sparse multidimensional representation using shearlets[J]. SIAM Journal on Mathematical Analysis, 2007, 39(1):298-318.[12] Wang Zhou, Bovik Alan C, Sheikh Hamid R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.[13] Eskicioglu A M, Fisher P S. Image quality measures and their performance[J]. IEEE Transactions on Communications, 1995, 43(12): 2959-2965.[14] Leung L W, King B, Vohora V. Comparison of image data fusion techniques using entropy and INI[C]//Proc of 22nd Asian Conference on Remote Sensing, Singapore, 2001: 152-157.[15] Qu Gui-hong, Zhang Da-li, Yan Ping-fan. Information measure for performance of image fusion[J]. Electronic Letters, 2002, 38(7):313-315.[16] Xydeas C S, Petrovi V. Objective image fusion performance measure[J]. Electronics Letters, 2000, 36(4):308-309.[17] Zhang Zhong, Blum Rick S. A categorization of multiscale decomposition-based image fusion schemes with a performance study for a digital camera application[J]. Proceedings of the IEEE, 1999, 87(8): 1315-1326.[18] 王朝晖, 王佳琪, 赵德功, 等. 基于Shearlet与改进PCNN的图像融合[J].激光与红外, 2012, 42(2):213-216. Wang Zhao-hui, Wang Jia-qi, Zhao De-gong, et al.Image Fusion based on Shearlet and improved PCNN[J]. Laser & Infrared, 2012, 42(2):213-216.[19] 叶传奇, 王宝树, 苗启广. 基于NSCT变换的红外与可见光图像融合算法[J].系统工程与电子技术, 2008, 30(4): 593-596. Ye Chuan-qi, Wang Bao-shu, Miao Qi-guang. Fusion algorithm of infrared and visible light images based on NSCT transform[J]. Systems Engineering and Electronics, 2008, 30(4):593-596. |
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