An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform
An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform
复制标题
基于相位相关的图像配准的扩展以使用多重极傅里叶变换来估计相似性变换
DOI：
10.3390/rs10111719
复制
发表时间：
2018
期刊：
影响因子：
5
通讯作者：
Chengjuan Gong
中科院分区：
文献类型：
--
作者：
Yunyun Dong;Weili Jiao;Tengfei Long;Guojin He;Chengjuan Gong
图像配准是众多图像处理领域的核心技术，在遥感领域有着广泛的应用。图像配准的准确性在很大程度上决定了后续应用的效果。近年来，基于相位相关的图像配准方法以其较高的精度和效率以及对灰度差异和内容的微小变化具有较强的稳健性而备受关注。许多研究人员已经报道，相位相关方法可以获得1/10甚至1/100的亚像素精度。然而，它的性能只能在翻译的情况下获得，这限制了该方法的应用范围。然而，基于相位相关方法的尺度和角度估计的报道却很少。为了利用基于相位相关的图像配准的高精度等优点，并将其扩展到相似性变换的估计，提出了一种新的算法--多层极傅立叶变换(MPFT)，该算法使用具有不同比例因子的快速而准确的极傅立叶变换来计算对数极傅里叶变换。MPFT的极网格结构与对数极网格的结构更为相似。特别是，仅对于旋转估计，MPFT的极网格是计算网格。为了验证其在角度和尺度估计方面的有效性和高精度，进行了定性和定量实验。定量实验包括数值模拟、合成数据实验和真实数据实验。实验结果表明，该方法优于现有的基于相位相关的相似变换估计方法伪极傅里叶变换(PPFT)和多层分数傅立叶变换(MLFFT)，以及经典的基于特征的配准方法尺度不变特征变换(SIFT)及其变种MS-SIFT。
Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.