提出FILER方法，解决多模态图像匹配中的非线性差异与旋转敏感问题。
Multimodal Image Matching based on Frequency-domain Information of Local Energy Response
- 基于频域局部能量响应建模，抑制非线性强度差异影响。
- 在多组医学影像配准中，准确率优于现有主流方法。
- 对几何扭曲、噪声和旋转均具备强鲁棒性，适合医疗图像应用。
复杂非线性强度差异、局部几何畸变、噪声及旋转变换是多模态图像匹配的主要挑战。为解决这些问题，本文提出基于局部能量响应频域信息的FILER方法。其核心是基于频域信息的局部能量响应模型，可有效克服非线性强度差异的影响。为提升对局部非线性几何畸变和噪声的鲁棒性，分别设计了边缘结构增强型特征检测器与卷积加权特征描述子。此外，FILER克服了频域信息对旋转角度的敏感性，实现旋转不变性。大量实验在多组多模态图像对上验证了FILER性能超越现有先进算法，具备良好鲁棒性与通用性。
原文摘要 · Abstract (English)
Complicated nonlinear intensity differences, nonlinear local geometric distortions, noises and rotation transformation are main challenges in multimodal image matching. In order to solve these problems, we propose a method based on Frequency-domain Information of Local Energy Response called FILER. The core of FILER is the local energy response model based on frequency-domain information, which can overcome the effect of nonlinear intensity differences. To improve the robustness to local nonlinear geometric distortions and noises, we design a new edge structure enhanced feature detector and convolutional feature weighted descriptor, respectively. In addition, FILER overcomes the sensitivity of the frequency-domain information to the rotation angle and achieves rotation invariance. Extensive experiments multimodal image pairs show that FILER outperforms other state-of-the-art algorithms and has good robustness and universality.
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