Zhu Lin, Mao Yuxing, Chen Chunxu, Ning Lanjia
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
J Imaging. 2025 Jan 13;11(1):23. doi: 10.3390/jimaging11010023.
In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results.
在电网智能巡检系统中，电力场景下红外图像与可见光图像的自动配准是一项关键的研究技术。由于可见光图像和红外图像在关键属性上存在明显差异，直接对齐往往难以达到预期效果。为克服红外图像与可见光图像对齐的高难度，本文提出了一种图像对齐方法。首先，我们使用Sobel算子提取图像对的边缘信息。其次，通过曲率尺度空间（CSS）角点检测器识别边缘中的特征点。第三，提取方向梯度直方图（HOG）作为特征点的梯度分布特征，并用尺度不变特征变换（SIFT）算法进行归一化，以形成特征描述符。最后，分别通过改进的快速近似最近邻匹配方法和自适应阈值处理实现初始匹配和精确匹配。实验表明，该方法能够在旋转、尺度和视角差异下稳健地匹配图像对的特征点，并取得了优异的匹配效果。
