Hierarchical matching for visible and infrared images of power equipment based on multiscale local normalized filtering

The matching and fusion of visible and infrared images of power equipment are essential for real-time monitoring. Nevertheless, the computation of an accurate transform model between images for alignment remains a tough challenge due to notable changes in spectrum, resolution, and intensity between visible and infrared images. To address this issue, this paper proposes a hierarchical matching algorithm for visible and infrared images of power equipment based on multiscale local normalized filtering. First, the input image is preprocessed by multi-scale local normalized filtering to enhance the image structures. Then, the contours of the filtered image are extracted by the Canny operator. A k-cosine curvature-based multi-scale corner detection algorithm is used to detect the feature points on the contours and the scale-invariant PIIFD is constructed for each feature point. Finally, a hierarchical matching strategy is proposed for feature matching, and the consistency check algorithm is applied to eliminate the false matches. The experiment is carried out on four groups of visible and infrared images of power equipment. The results show that our method can effectively achieve a large number of correct matches despite of scale and viewpoint changes.