图像匹配能将待匹配图像变换到原有图像的坐标系中，在各种视觉任务中起着重要的作用。基于特征的图像匹配算法能够在图像中匹配到一些更具区分度的特征，与其他图像匹配方法相比，其具有高精度、高灵活性、高鲁棒性等特点。针对传统特征匹配算法匹配稀疏的问题，提出一种基于改进深度特征匹配算法的密集特征匹配方法。首先，通过VGG网络提取图像的一系列特征图，在初始特征图进行最邻近匹配计算单应性矩阵并进行视角变换；然后，基于特征图的频域匹配特点进行深层特征图融合，用于特征粗匹配；最后，基于粗匹配的结果在浅层特征图上进行特征细匹配用于校正特征匹配的结果。实验结果表明：所提算法提升了特征匹配的精度和匹配的特征数量。

Image matching, which refers to transforming the image to be matched into the coordinate system of the original image, plays important roles in numerous visual tasks. The feature-based image matching method, which can find distinctive features in the image, is widely accepted because of its applicability, robustness, and high accuracy. For improving the performance of feature matching, it is important to obtain more feature matches with high matching accuracy. Aiming at the sparse matching problem of the traditional feature matching algorithm, we propose a dense feature matching method based on the improved deep feature matching algorithm. First, a series of feature maps of the image are extracted through the VGG neural network, and nearest-neighbor matching is performed on the initial feature map to calculate the homography matrix and perform perspective transformation. Then, deep features are fused according to the frequency-domain matching characteristics of feature maps for coarse feature matching. Finally, fine feature matching is performed on the shallow feature map to correct the results of coarse feature matching. Experimental results indicate that the proposed algorithm is superior to other methods, as it obtains a larger number of matches with a higher matching accuracy.