提出一种高精度联合估计图像缩放与旋转的算法。
Scale and Rotation Estimation of Similarity-Transformed Images via Cross-Correlation Maximization Based on Auxiliary Function Method
- 基于对数极坐标傅里叶变换与交叉相关最大化
- 亚像素级精度，均方误差低于传统方法
- 适用于医学影像与计算机视觉中的图像配准
本文提出一种高效算法，可联合估计两幅图像间的缩放与旋转，达到亚像素级精度。图像配准是不同视角下图像空间对齐的关键步骤，在医学影像与计算机视觉中广泛应用。传统相位相关法仅能有效处理平移变化，难以应对因相机变焦或旋转带来的缩放与旋转变化。本文提出的新算法结合对数极坐标下的傅里叶变换与交叉相关最大化策略，并利用辅助函数法优化，通过引入亚像素级交叉相关，实现对缩放与旋转的精确估计。实验结果表明，该方法在缩放与旋转估计上的均值误差低于依赖离散交叉相关的传统傅里叶方法。
原文摘要 · Abstract (English)
This paper introduces a highly efficient algorithm capable of jointly estimating scale and rotation between two images with sub-pixel precision. Image alignment serves as a critical process for spatially registering images captured from different viewpoints, and finds extensive use in domains such as medical imaging and computer vision. Traditional phase-correlation techniques are effective in determining translational shifts; however, they are inadequate when addressing scale and rotation changes, which often arise due to camera zooming or rotational movements. In this paper, we propose a novel algorithm that integrates scale and rotation estimation based on the Fourier transform in log-polar coordinates with a cross-correlation maximization strategy, leveraging the auxiliary function method. By incorporating sub-pixel-level cross-correlation our method enables precise estimation of both scale and rotation. Experimental results demonstrate that the proposed method achieves lower mean estimation errors for scale and rotation than conventional Fourier transform-based techniques that rely on discrete cross-correlation.
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