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首页> 外文期刊>Journal of visual communication & image representation >Gradient-based subspace phase correlation for fast and effective image alignment
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Gradient-based subspace phase correlation for fast and effective image alignment

机译：基于梯度的子空间相位相关性，可实现快速有效的图像对齐

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摘要

Phase correlation is a well-established frequency domain method to estimate rigid 2-D translational motion between pairs of images. However, it suffers from interference terms such as noise and non-overlapped regions. In this paper, a novel variant of the phase correlation approach is proposed, in which 2-D translation is estimated by projection-based subspace phase correlation (SPC). Conventional wisdom has suggested that such an approach can only amount to a compromise solution between accuracy and efficiency. In this work, however, we prove that the original SPC and the further introduced gradient-based SPC can provide robust solution to zero-mean and non-zero-mean noise, and the latter is also used to model the interference term of non-overlapped regions. Comprehensive results from synthetic data and MRI images have fully validated our methodology. Due to its substantially lower computational complexity, the proposed method offers additional advantages in terms of efficiency and can lend itself to very fast implementations for a wide range of applications where speed is at a premium.
机译：相位相关是一种行之有效的频域方法，用于估计图像对之间的刚性2-D平移运动。但是，它受到干扰项的影响，例如噪声和非重叠区域。在本文中，提出了一种新的相位相关方法变体，其中通过基于投影的子空间相位相关（SPC）估计二维转换。传统观点认为，这种方法只能构成准确性和效率之间的折衷解决方案。但是，在这项工作中，我们证明了原始SPC和进一步引入的基于梯度的SPC可以为零均值和非零均值噪声提供鲁棒的解决方案，并且后者也可用于对非零均值噪声进行建模。重叠区域。综合数据和MRI图像的综合结果充分验证了我们的方法。由于其实质上较低的计算复杂度，因此所提出的方法在效率方面提供了其他优点，并且可以使其非常适用于速度极为宝贵的广泛应用。

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