基于辅助函数法的傅里叶-梅林变换相似变换图像配准
Alignment of Similarity-Transformed Images Based on Fourier--Mellin Transform Using Auxiliary Function Method
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中文总结 AI 辅助
该研究针对傅里叶-梅林配准的亚像素精度不足问题，提出结合对数极坐标谱估计与辅助函数法的两阶段配准算法，可降低相似变换的估计误差。
中文摘要 AI 辅助
本文提出一种亚像素精度的两图像间相似变换（即平移、缩放与旋转）估计算法。图像配准是对齐不同视角与成像条件下获取图像的基础技术，代表性方法是基于最大化离散互相关的傅里叶-梅林配准，但该方法在需亚像素级估计时往往无法达到足够对齐精度。所提方法整合了：1）对数极坐标表示下傅里叶幅度谱的缩放与旋转估计；2）基于辅助函数法的纯相位互相关最大化。该整合实现了两阶段估计流程：先估计不受平移影响的缩放与旋转，再用校正后的图像对在空间域以亚像素精度估计平移。对经随机相似变换的图像对的仿真实验表明，与基于离散互相关的傅里叶-梅林配准方法相比，所提方法降低了缩放、旋转及平移的估计误差。
英文摘要
This paper proposes an algorithm for estimating the similarity transformation, namely translation, scale, and rotation, between two images with subpixel accuracy. Image registration is a fundamental technique for aligning images acquired under different viewpoints and imaging conditions, and a representative approach based on maximizing discrete cross-correlation is the Fourier--Mellin registration. However, the Fourier--Mellin approach often fails to achieve sufficient alignment accuracy when subpixel-level estimation is required. The proposed method integrates (i) scale-and-rotation estimation from the Fourier magnitude spectrum in a log-polar representation and (ii) maximization of phase-only correlation based on the auxiliary function method. This integration enables a two-stage estimation procedure: it first estimates scale and rotation without being affected by translation, and then estimates translation with subpixel precision in the spatial domain using the corrected image pair. A simulation experiment on image pairs subjected to random similarity transformations demonstrates that the proposed method reduces estimation errors in scale, rotation, and translation compared with Fourier--Mellin-based registration methods using discrete cross-correlation.
发表机构
- Tokai University（东海大学）
- Tokyo Metropolitan University（东京都立大学）
机构由 AI 辅助整理，请以论文原文为准。
