基于局部频率信息和单纯型模拟退火的异源图像配准

非均态灰度变化和局部景象变化是异源图像配准面临的主要挑战。基于平均局部相位角(MLPA)和频率展开相位一致(FSPC)两种局部频率表达实现了对异源图像的非均态灰度和对比度反转不变的描述，并通过FSPC提取了图像间的公共结构信息；通过目标函数综合MLPA和FSPC的优势，对图像间公共结构信息分配更大的置信度；通过调整单纯型模拟退火算法，避免算法陷于局部最优解。大量真实和仿真实验证明了该方法能有效容忍图像间较大几何变形、景物差异和非均态灰度变化，改善了传统方法的配准精度和稳定性。

Abstract

The major challenges in registration between multi-modal images are the non-homogeneous intensity variation and the partial scene changes. Two local frequency representations, namely mean local phase angle (MLPA) and frequency spread phase congruent (FSPC), are used to achieve representations invariant to both non-homogeneous intensity variation and contrast reversal between multi-modal images. In addition, by using FSPC one can effectively emphasize the common structural information. An objective function is constructed to take full advantage of the two representations as well as allocate more confidences to the stable structures. Simplex-simulated annealing algorithm is adjusted to avoid being trapped in local optima. Numerous experiments using real and synthetic images clearly demonstrate that the proposed method can effectively register multi-modal images with significant variation in geometric distortion, non-homogeneous intensity and scene, as well as, improve the registration accuracy and robustness of the conventional methods.

参考文献

[1] J. Wang, T. Jiang. Nonrigid registration of brain MRI using nurbs [J]. Parallel Recogn. Lett., 2007, 28(2): 214～223

[2] 张肃, 王文生. 复杂背景下运动目标的光学相关识别[J]. 光学学报, 2012, 32(1): 0107001

Zhang Su, Wang Wensheng. Optical correlation recognition of the moving target in cluttered background [J]. Acta Optica Sinica, 2012, 32(1): 0107001

[3] 魏许, 徐贵力, 王彪等. 面向无人机自主导航的立体匹配算法[J]. 中国科学: 信息科学, 2012, 42 (11): 1338～1349

Wei Xu, Xu Guili, Wang Biao et al.. A new stereo matching algorithm for UAV autonomous navigation [J]. Sci. China Inf. Sci., 2012, 42(11): 1338～1349

[4] 柴勇, 何友, 曲长文. 基于亚像素区域加权能量特征的多尺度图像融合算法[J]. 光学学报, 2009, 29(10): 2732～2737

Chai Yong, He You, Qu Changwen. Multiscale image fusion algorithm based on subpixel weighted region energy [J]. Acta Optica Sinica, 2009, 29(10): 2732～2737

[5] X. C. Liu, Y. Shang, Z. H. Lei et al.. Change detection by local illumination compensation using local binary pattern [J]. Opt. Eng., 2012, 51(9): 097202

[6] J. D. Ouwerkerk. Image super-resolution survey [J]. Image Vis. Comput., 2006, 24(10): 1032～1052

[7] L.G. Brown. A survey of image registration techniques [J]. ACM Computing Surveys, 1992, 24(4): 326～376

[8] H. Li, B. S. Manjunath, S. K. Mitra. A contour based approach to multi-sensor image registration [J]. IEEE Trans. Image Processing, 1995, 4(3): 320～334

[9] J. M. Wu, Z. L. Jing, Z. Wu et al.. Study on an improved Hausdorff distance for multi-sensor image matching [C]. Communications in Nonlinear Science and Numerical Simulation, 2010, 17(2): 513～520

[10] X. M. Peng, M. Y. Ding, C. P. Zhou et al.. A practical two-step image registration method for two-dimensional images [J]. Information Fusion, 2004, 5(4): 283～298

[11] S. H. Jiang, C. Wang, X. J. Xu. Fast algorithm for multisource image registration based on geometric feature of corners [J]. Advanced Intelligent Computing Theories and Applications, 2010, 6215: 438～446

[12] 张锐娟, 张建奇, 杨翠等. 基于CSIFT的彩色图像配准技术研究[J]. 光学学报, 2008, 28(11): 2097～2103

Zhang Ruijuan, Zhang Jianqi, Yang Cui et al.. Study on color image registration technique based on CSIFT [J]. Acta Optica Sinica, 2008, 28(11): 2097～2103

[13] R. Eastman, J. L. Moigne. Gradient-descent techniques for multi-temporal and multi-sensor image registration of remotely sensed imagery [C]. Proc. FUSION′ 2001, 4th Int. Conf. Information Fusion, 2001

[14] J. R. Bergen, P. Anadan, K. J. Hanna et al.. Hierarchical model-based motion estimation [C]. Proceedings of the 2ndEuropean Conference on Computer Vision, Santa Margherita, Italy, 1992: 237～252

[15] R. K. Sharma, M. Pavel. Multi-sensor image registration [C]. SID Digest of Society for Information Display (XXVIII), 1997: 951～954

[16] M. Irani, P. Anadan. Robust multi-sensor image alignment [C]. Proceedings of 6th International Conference on Computer Vision, Bombay, India, 1998: 959～966

[17] P. Viola, W. Wells III. Alignment by maximization of mutual information [C]. International Conference on Computer Vision, Cambridge, MA, 1995: 16～23

[18] Y. S. Kim, J. H. Lee, J. B. Ra. Multi-sensor image registration based on intensity and edge orientation information [J]. Pattern Recognition, 2008, 41(11): 3356～3365

[19] P. Thevenaz, M. Unser. Optimization of mutual information for multi-resolution image registration [J]. IEEE Trans. Image Processing, 2000, 9(12): 2083～2099

[20] J. Liu, B. C. Vemuri, F. Bova. Efficient multi-modal image registration using local-frequency maps [J]. Machine Vision and Applications, 2002, 13(3): 149～163

[21] J. Morlet, G. Arens, E. Fourgeau et al.. Wave propagation and sampling theory-Part II: Sampling theory and complex waves [J]. Geophysics, 1982, 47(2): 222～236

[22] S. Yong, H. Jae, B. Jong. Multi-sensor image registration based on intensity and edge orientation information [J]. Pattern Recognition, 2008, 41(11): 3356～3365

[23] M. C. Morrone, J. R. Ross, D. C. Burr et al.. Mach bands are phase dependent [J]. Nature, 1986, 324(6094): 250～253

[24] M. C. Morrone, R. A. Owens. Feature detection from local energy [J]. Pattern Recognition Letters, 1987, 6(5): 303～313

[25] M.F. Cardoso, R. L. Salcedo, S. F. Azevedo. The simplex-simulated annealing approach to continuous non-linear optimization [J]. Computers Chem. Eng., 1996, 20(9): 1065～1080

[26] J.C. Lagarias, J. A. Reeds, M. H. Wright et al.. Convergence properties of the Nelder-Mead simplex method in low dimensions [J]. SIAM J. Optim., 1998, 9(1): 112～147

[27] N. D. Cahill. Normalized measures of mutual information with general definitions of entropy for multimodal image registration [C]. WBIR 2010, LNCS 6204, 2010: 258～268

[28] A. Wong, W. Bishop. Efficient least squares fusion of MRI and CT images using a phase congruency model [J]. Pattern Recognition Letters, 2008, 29(3): 173～180

[29] M. Elbakary, M. K. Sundareshan. Accurate representation of local frequency using computationally efficient Gabor filter fusion approach with application to image registration [J]. Pattern Recognition Letters, 2005, 26(14): 2164～2173

刘晓春, 钟涛, 于起峰, 张小虎, 雷志辉, 尚洋. 基于局部频率信息和单纯型模拟退火的异源图像配准[J]. 光学学报, 2013, 33(6): 0615002. Liu Xiaochun, Zhong Tao, Yu Qifeng, Zhang Xiaohu, Lei Zhihui, Shang Yang. Multi-Modal Image Registration Based on Local Frequency Information Using Modified Simplex-Simulated Annealing Algorithm[J]. Acta Optica Sinica, 2013, 33(6): 0615002.

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