In the infrared- and visible-image alignment of power equipment, severe nonlinear radial aberrations as well as significant viewing-angle and scale differences occur between the infrared and visible images, which result in image-alignment failure. Hence, a local normalization-based algorithm for the infrared- and visible-image alignment of power equipment is proposed. First, local normalization was performed to eliminate the nonlinear distortion of the images and improve the accuracy of the curvaturescale space (CSS) algorithm in extracting the feature points. Subsequently, the main direction of the feature points was calculated based on the local curvature information, and the multiscale oriented gradient histogram (MSHOG) was used as the feature descriptor. Finally, the features were matched using the proposed accurate matching method to obtain the parameters of the inter-image projective transformations. The proposed algorithm has average root mean square errors of 2.18 and 2.24 and average running times of 13.09 s and 12.07 s under infrared- and visible-image datasets of electric power equipment, respectively. Experimental results verify the effectiveness of the method in addressing images to be aligned with obvious differences in viewpoints and proportions, as well as in realizing the high-precision alignment of infrared and visible images of electric power equipment.