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Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based on Phase Congruency

机译:基于相位一致性的基于照明-鲁棒亚像素傅立叶的图像相关方法

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The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.
机译:基于傅立叶的图像相关技术由于其准确性,效率以及对图像对比度和亮度的鲁棒性而受到广泛关注。因此,已经提出了各种子像素方法。但是,复杂的辐射度变化的详细子像素级影响尚待研究,并且很少做出相应的改进。本文提出了一种基于相位一致性的照明鲁棒子像素傅里叶图像相关方法。相位一致性特征的大小和方向信息都被用来构造结构图像表示。然后,通过频域中的线性相位估计或空间域中的核拟合,将图像表示嵌入到子像素方法的相关方案中,从而实现两种改进的子像素方法。所提出的方法结合了结构图像表示和原始相关方案的优点,并充分利用全局和局部相位信息来实现鲁棒性相关。利用真实的亚像素位移进行了模拟和实际辐射差异的实验。评估并比较了所提出的方法和其他基于傅立叶的子像素相关方法的性能。实验结果表明,在存在多种辐射变化的情况下,所提方法在准确性和鲁棒性方面均优于其他方法。

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