基于不可分小波分解的图像配准方法
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摘要
张量积小波强调的是图像中水平和垂直方向的高频信息,而不可分小波具有各向同性,可以提取图像中各个方向的边缘,能获得比较完整的图像轮廓,将这种特点应用于图像配准时,能准确定位图像仿射不变点的位置。为此,提出一种通过求取不可分小波分解后的高频子图像配准参数来配准原图像的方法,把图像的配准问题转化为其不可分小波分解后的高频子图像配准问题。从不可分小波分解的快速算法理论出发,证明该配准方法的正确性。构造一组四通道不可分小波滤波器组,在此基础上给出配准的方法和步骤。实验结果表明,该方法具有较好的配准效果,其求取图像配准参数的运算量比直接求取原图像配准参数运算量的1/4还少,与基于张量积小波分解的图像配准方法相比,具有较高的配准精度。
Tensor product wavelet only emphasizes on the edge of the horizontal and vertical direction. Nonseparable wavelet is isotropic,can extract the edge of the image in all directions,and can obtain relatively complete outline of the image. When this kind of characteristics is applied to the image registration,it can acquire the accurate positions of the invariant points of the affine transform. Based on this characteristic,this paper proposes an image registration method through calculating the registration parameters of the high-frequency sub-images of the nonseparable wavelet decomposition of the original image. This method can transform the registration of the original image into the registration of its high-frequency sub-images. The correctness of the proposed registration method is proved according to the fast algorithm theory of wavelet decomposition. A four channel nonseparable wavelet filter bank is constructed and the registration steps of the method are given. Experimental results show that the method has better registration results. The amount of computation for computing the registration parameter is less than a quarter of the registration parameters computation of the original image. When compared with registration method based on the tensor product wavelet decomposition,the method has higher registration precision.
Tensor product wavelet only emphasizes on the edge of the horizontal and vertical direction. Nonseparable wavelet is isotropic,can extract the edge of the image in all directions,and can obtain relatively complete outline of the image. When this kind of characteristics is applied to the image registration,it can acquire the accurate positions of the invariant points of the affine transform. Based on this characteristic,this paper proposes an image registration method through calculating the registration parameters of the high-frequency sub-images of the nonseparable wavelet decomposition of the original image. This method can transform the registration of the original image into the registration of its high-frequency sub-images. The correctness of the proposed registration method is proved according to the fast algorithm theory of wavelet decomposition. A four channel nonseparable wavelet filter bank is constructed and the registration steps of the method are given. Experimental results show that the method has better registration results. The amount of computation for computing the registration parameter is less than a quarter of the registration parameters computation of the original image. When compared with registration method based on the tensor product wavelet decomposition,the method has higher registration precision.
引文
[1]Li Yang,Verma R.Multichannel Image Registration by Feature-based Information Fusion[J].IEEE Transactions on Medical Imaging,2011,30(3):707-720.
[2]Wen Gongjian,Lv Jinjian,Yu Wenxian.A High-performance Feature-matching Method for Image Registration by Combining Spatial and Similarity Information[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(4):1266-1277.
[3]Lu G,Yan J,Kou Y,et al.Image Registration Based on Criteria of Feature Point Pair Mutual Information[J].IET Image Processing,2011,5(6):560-566.
[4]朱冰莲,田学隆,宋维杰.基于人工免疫系统的医学图像配准[J].仪器仪表学报,2009,30(7):1416-1419.
[5]徐志刚,苏秀琴.基于小波分解与多约束改进的序列图像配准[J].仪器仪表学报,2011,32(10):2261-2266.
[6]王阿妮,马彩文,刘爽,等.基于角点的红外与可见光图像自动配准方法[J].光子学报,2009,38(12):3328-3332.
[7]李映,崔扬扬,韩晓宇.基于线特征和控制点的可见光和SAR图像配准[J].自动化学报,2012,38(12):1968-1974.
[8]左欣,戴修斌,张辉,等.基于Legendre正交矩的模糊形变图像的配准方法[J].电子学报,2011,39(12):2824-2830.
[9]谌安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J].电子与信息学报,2004,26(5):679-684.
[10]刘斌,彭嘉雄.图像配准的小波分解方法[J].计算机辅助设计与图形学学报,2003,15(9):1070-1073.
[11]Liu Bin,Peng Jiaxiong.Image Fusion Method Based on Nonseparable Wavelets[J].Machine Vision and Applications,2005,16(3):189-196.
[12]刘斌,彭嘉雄.基于四通道不可分加性小波的多光谱图像融合[J].计算机学报,2009,32(2):350-356.
[13]刘斌,刘维杰,马嘉利.基于三通道不可分对称小波的多聚焦图像融合[J].仪器仪表学报,2012,33(5):1110-1116.
[2]Wen Gongjian,Lv Jinjian,Yu Wenxian.A High-performance Feature-matching Method for Image Registration by Combining Spatial and Similarity Information[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(4):1266-1277.
[3]Lu G,Yan J,Kou Y,et al.Image Registration Based on Criteria of Feature Point Pair Mutual Information[J].IET Image Processing,2011,5(6):560-566.
[4]朱冰莲,田学隆,宋维杰.基于人工免疫系统的医学图像配准[J].仪器仪表学报,2009,30(7):1416-1419.
[5]徐志刚,苏秀琴.基于小波分解与多约束改进的序列图像配准[J].仪器仪表学报,2011,32(10):2261-2266.
[6]王阿妮,马彩文,刘爽,等.基于角点的红外与可见光图像自动配准方法[J].光子学报,2009,38(12):3328-3332.
[7]李映,崔扬扬,韩晓宇.基于线特征和控制点的可见光和SAR图像配准[J].自动化学报,2012,38(12):1968-1974.
[8]左欣,戴修斌,张辉,等.基于Legendre正交矩的模糊形变图像的配准方法[J].电子学报,2011,39(12):2824-2830.
[9]谌安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J].电子与信息学报,2004,26(5):679-684.
[10]刘斌,彭嘉雄.图像配准的小波分解方法[J].计算机辅助设计与图形学学报,2003,15(9):1070-1073.
[11]Liu Bin,Peng Jiaxiong.Image Fusion Method Based on Nonseparable Wavelets[J].Machine Vision and Applications,2005,16(3):189-196.
[12]刘斌,彭嘉雄.基于四通道不可分加性小波的多光谱图像融合[J].计算机学报,2009,32(2):350-356.
[13]刘斌,刘维杰,马嘉利.基于三通道不可分对称小波的多聚焦图像融合[J].仪器仪表学报,2012,33(5):1110-1116.