In view of the multi-scale changes and the influence of light and angle in the image matching process, it is quite difficult to realize intelligent image registration by using convolutional neural network. The existing image matching algorithm has the following problems in the application process: the existing shallow feature extraction model has lost a lot of effective feature information and low recognition accuracy. Meanwhile, the image registration method based on deep learning is not robust and accurate enough. Therefore, an image registration method based on additive edge cosine loss was proposed in this paper. In the twin network architecture, cosine loss was used to convert Euclidean space into angular space, which eliminated the influence of characteristic intensity and improved the accuracy of registration. The matching cost was directly calculated by the included angle of two vectors in the embedded space, where the size of the angle edge could be quantitatively adjusted through parameter \(m\). We further derived a specific \(m\) to quantitatively adjust the loss. In addition, anti-rotation attention mechanism was added to the network to enhance the ability of feature information extraction and adjust the position information of feature vectors to reduce the mismatching caused by image rotation.

鉴于图像匹配过程中的多尺度变化以及光线和角度的影响，利用卷积神经网络很难实现智能图像配准。现有的图像匹配算法在应用过程中存在以下问题：现有的浅层特征提取模型失去了大量有效的特征信息，识别精度低。同时，基于深度学习的图像配准方法不够鲁棒和准确。因此，本文提出了一种基于累加边缘余弦损失的图像配准方法。在双网架构中，利用余弦损失将欧几里得空间转换为角空间，从而消除了特征强度的影响，提高了配准的准确性。\（m \）。我们进一步推导了一个特定的\（m \）来定量调整损失。另外，网络中增加了防旋转注意机制，以增强特征信息提取的能力，并调整特征向量的位置信息，以减少由图像旋转引起的不匹配。