一种改进的基于奇异值分解的亚像素级图像配准算法
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摘要
基于奇异值分解(SVD)的相位相关法是一种经典的具有亚像素级精度的图像配准算法,但当两幅待配准图的平移量较大或噪声较强时,该配准算法中的积分法得到的相位解缠结果往往不可靠。根据线性相位的单调变化特性,提出一种改进的相位解缠算法,通过比较相邻相位差和趋势斜率的一致性判断是否进行校正,从而得到真实相位值。针对真实光学图像的实验表明,该方法可以有效地对积分法所得到的结果进行校正,进而提高匹配精度。
Phase correlation method based on singular value decomposition(SVD) is a known subpixel image registration algorithm. However, when the translation between two images to be registered is large or there is high noise, the phase unwrapping results obtained using the integral method in the registration algorithm are often unreliable. In this work, an improved phase unwrapping algorithm is proposed based on the monotonic variation of linear phase. By comparing the consistency of the adjacent phase differences and the trend slopes, the algorithm determines whether to make corrections, so as to get the real phase. Experiments on real optical images show that the proposed method effectively corrects the results obtained using the integral method, and hence increases matching accuracy.
Phase correlation method based on singular value decomposition(SVD) is a known subpixel image registration algorithm. However, when the translation between two images to be registered is large or there is high noise, the phase unwrapping results obtained using the integral method in the registration algorithm are often unreliable. In this work, an improved phase unwrapping algorithm is proposed based on the monotonic variation of linear phase. By comparing the consistency of the adjacent phase differences and the trend slopes, the algorithm determines whether to make corrections, so as to get the real phase. Experiments on real optical images show that the proposed method effectively corrects the results obtained using the integral method, and hence increases matching accuracy.
引文
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[5] 卢浩, 刘团结, 尤红建. 亚像素级的图像配准方法[J]. 国外电子测量技术, 2012, 31(4): 45-49.
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[8] Yousef A, Li J, Karim M. High-speed image registration algorithm with subpixel accuracy[J]. IEEE Signal Processing Letters, 2015, 22(10): 1 796-1 800.
[9] Thevenaz P, Unser M. Optimization of mutual information for multiresolution image registration[J]. IEEE Transaction on Image Processing, 2000, 9(12): 2 083-2 099.
[10] 徐宝昌, 陈哲, 赵龙, 一种改进的最小二乘景象匹配算法[J]. 北京航空航天大学学报, 2005, 31(8): 848-852.
[11] Hoge W S. A subspace identification extension to the phase correlation method[J]. IEEE Trans on Medical Imaging, 2003, 22(2): 277-280.
[12] Tong X, Ye Z, Xu Y, et al. A noval subpixel phase correlation method using singular value decomposition and unified random sample consensus[J]. IEEE Trans on Geoscience and Remote Sensing, 2015, 63(3): 207-224.
[13] Feng S, Deng L, Shu G, et al. A subpixel registration algorithm for low PNSR images[C]//2012 IEEE fifth International Conference on Advanced Computational Intelligence, Nanjing, 2012: 626-630.
[14] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[M]. United States of America: ACM, 1981, 24(6): 726-740.
[15] Zitová B, Flusser J. Image registration methods: a survey[J]. Image and Vision Computing, 2003, 21(11): 977-1 000.
[16] Itoh K. Analysis of the phase unwrapping algorithm[J]. Applied Optics, 1982, 21(14): 2 470.
[17] 邓晓龙. InSAR相位解缠算法研究[D]. 哈尔滨:哈尔滨工业大学, 2013.
[18] Foroosh H, Zerubia J B, Berthod M. Extension of phase correlation to subpixel registration[J]. IEEE Transactions on Image Processing, 2002, 11(3): 188-200.
[2] Stone H S, Orchard M T, Chang E, et al. A fast direct Fourier-based algorithm for subpixel registration of images[J]. IEEE Trans on Geoscience and Remote Sensing, 2001, 39(10):2 235-2 243.
[3] Muquit M A, Shibahara T. A high-accuracy passive 3D measurement system using phase-based image matching[J]. Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2006, 89(3): 686-697.
[4] 黎俊, 彭启民, 范植华. 亚像素级图像配准算法研究[J]. 中国图像图像学报, 2008, 13(11): 2 070-2 075.
[5] 卢浩, 刘团结, 尤红建. 亚像素级的图像配准方法[J]. 国外电子测量技术, 2012, 31(4): 45-49.
[6] Tian Q, Huhns M N. Algorithms for subpixel registration[J]. Computer Vision, Graphics, and Image Processing, 1986, 35(2): 220-233.
[7] Guizar-Sicairos M, Thurman S T, Fienup J R. Efficient subpixel image registration algorithms[J]. Optics Letters, 2008,33(2):156-158.
[8] Yousef A, Li J, Karim M. High-speed image registration algorithm with subpixel accuracy[J]. IEEE Signal Processing Letters, 2015, 22(10): 1 796-1 800.
[9] Thevenaz P, Unser M. Optimization of mutual information for multiresolution image registration[J]. IEEE Transaction on Image Processing, 2000, 9(12): 2 083-2 099.
[10] 徐宝昌, 陈哲, 赵龙, 一种改进的最小二乘景象匹配算法[J]. 北京航空航天大学学报, 2005, 31(8): 848-852.
[11] Hoge W S. A subspace identification extension to the phase correlation method[J]. IEEE Trans on Medical Imaging, 2003, 22(2): 277-280.
[12] Tong X, Ye Z, Xu Y, et al. A noval subpixel phase correlation method using singular value decomposition and unified random sample consensus[J]. IEEE Trans on Geoscience and Remote Sensing, 2015, 63(3): 207-224.
[13] Feng S, Deng L, Shu G, et al. A subpixel registration algorithm for low PNSR images[C]//2012 IEEE fifth International Conference on Advanced Computational Intelligence, Nanjing, 2012: 626-630.
[14] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[M]. United States of America: ACM, 1981, 24(6): 726-740.
[15] Zitová B, Flusser J. Image registration methods: a survey[J]. Image and Vision Computing, 2003, 21(11): 977-1 000.
[16] Itoh K. Analysis of the phase unwrapping algorithm[J]. Applied Optics, 1982, 21(14): 2 470.
[17] 邓晓龙. InSAR相位解缠算法研究[D]. 哈尔滨:哈尔滨工业大学, 2013.
[18] Foroosh H, Zerubia J B, Berthod M. Extension of phase correlation to subpixel registration[J]. IEEE Transactions on Image Processing, 2002, 11(3): 188-200.