Multimodal image matching is essential in image stitching, image fusion, change detection, and land cover mapping. However, the severe nonlinear radiometric distortion (NRD) and geometric distortions in multimodal images severely limit the accuracy of multimodal image matching, posing significant challenges to existing methods. Additionally, detector-based methods are prone to feature point offset issues in regions with substantial modal differences, which also hinder the subsequent fine registration and fusion of images. To address these challenges, we propose a guided refinement for detector-free multimodal image matching (GRiD) method, which weakens feature point offset issues by establishing pixel-level correspondences and utilizes reference points to guide and correct matches affected by NRD and geometric distortions. Specifically, we first introduce a detector-free framework to alleviate the feature point offset problem by directly finding corresponding pixels between images. Subsequently, to tackle NRD and geometric distortion in multimodal images, we design a guided correction module that establishes robust reference points (RPs) to guide the search for corresponding pixels in regions with significant modality differences. Moreover, to enhance RPs reliability, we incorporate a phase congruency module during the RPs confirmation stage to concentrate RPs around image edge structures. Finally, we perform finer localization on highly correlated corresponding pixels to obtain the optimized matches. We conduct extensive experiments on four multimodal image datasets to validate the effectiveness of the proposed approach. Experimental results demonstrate that our method can achieve sufficient and robust matches across various modality images and effectively suppress the feature point offset problem.

中文翻译：

---

GRiD：用于无探测器多模态图像匹配的引导优化


多模态图像匹配在图像拼接、图像融合、变化检测和土地覆盖制图中至关重要。然而，多模态图像中严重的非线性辐射畸变 （NRD） 和几何畸变严重限制了多模态图像匹配的精度，对现有方法提出了重大挑战。此外，基于探测器的方法在模态差异较大的区域容易出现特征点偏移问题，这也阻碍了后续图像的精细配准和融合。为了应对这些挑战，我们提出了一种无检测器多模态图像匹配 （GRiD） 方法的指导改进，该方法通过建立像素级对应来削弱特征点偏移问题，并利用参考点来指导和纠正受 NRD 和几何失真影响的匹配。具体来说，我们首先引入了一个无检测器框架，通过直接查找图像之间的相应像素来缓解特征点偏移问题。随后，为了解决多模态图像中的 NRD 和几何失真，我们设计了一个引导校正模块，该模块建立了稳健的参考点 （RPs），以指导在具有显着模态差异的区域中搜索相应的像素。此外，为了提高 RPs 的可靠性，我们在 RPs 确认阶段加入了一个相位一致性模块，以将 RPs 集中在图像边缘结构周围。最后，我们对高度相关的相应像素进行更精细的定位，以获得优化的匹配。我们在四个多模态图像数据集上进行了广泛的实验，以验证所提出的方法的有效性。 实验结果表明，我们的方法可以在各种模态图像上实现充分和稳健的匹配，并有效地抑制了特征点偏移问题。