Image registration plays a pivotal role in various image processing applications, which is widely used in image fusion and change detection. However, The presence of speckle noise in SAR images causes a primary reduction in registration accuracy and existing algorithms have not achieved high-precision registration while maintaining low computational complexity. This letter proposes an image registration algorithm based on edge intersections and deep-learning descriptors. An edge-directed voting mechanism is introduced to identify corner points, and a custom SAR image dataset is constructed to retrain the HardNet descriptor network. Experimental results validate the superiority of the proposed method in terms of robustness and accuracy, achieving SAR image registration on a self-constructed dataset with an RMSE of 0.38, showcasing the utmost registration accuracy, while maintaining lower computational complexity than traditional approaches.

中文翻译：

---

一种基于边缘交集提取和再训练HardNet的SAR图像配准算法

图像配准在各种图像处理应用中起着举足轻重的作用，广泛应用于图像融合和变化检测。然而，SAR图像中散斑噪声的存在导致配准精度初级降低，现有算法未能在保持较低计算复杂度的同时实现高精度配准。这封信提出了一种基于边缘交叉和深度学习描述符的图像配准算法。引入边缘定向投票机制来识别角点，并构建自定义 SAR 图像数据集来重新训练 HardNet 描述符网络。实验结果验证了该方法在鲁棒性和准确性方面的优越性，在自建数据集上实现了SAR图像配准，RMSE为0.38，展示了最大的配准精度，同时保持了比传统方法更低的计算复杂度。