It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.
Near Infrared and Visible Image Registration using Whale Optimization Algorithm
