# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 1 Introduction  
Infrared and visible image match aims to establish the correspondence of feature points between images formed by different spectrums. Visible images can obtain the fine details of scenes and infrared images can obtain coarse structures of scenes even under the condition of limited light like nighttime and fog [1]. Infrared and visible image match can provide complementary information captured by multi-modality images. [PERSON] et al. [2] utilize infrared thermal images to measure the temperature of neonates. [PERSON] et al. [3] utilize the infrared image to carry out the night-time navigation. These methods use infrared images to accomplish the tasks that are impossible for visible images. The infrared and visible image match has been widely applied in an unmanned aerial vehicle [3, 4], remote sensing satellite [5, 6], and security monitoring platform [7, 8]. The infrared and visible image match is still a difficult problem even though single-modality image matching methods have been extensively studied [9]. Visible images capture reflected light with spectrum 0.4-0.7 \(\upmu\)m and infrared images capture thermal radiation with a spectrum of 0.75-15 \(\upmu\)m [1]. Due to different imaging mechanisms, there are significant differences in image contents between infrared and visible images. The differences of image contents include nonlinearintensity variations [10; 11] and local detail discrepancies [12]. Single-modality image matching methods can be divided into two types such as hand craft based methods like SIFT (Scale Invariant Feature Transform) [13] and ORB (Oriented FAST and Rotated BRIEF) [14] and deep learning based methods like LIFT (Learned Invariant Feature Transform) [15] and SuperPoint [16]. Hand craft-based methods are designed according to the prior knowledge on single-modality images. Deep learning-based methods are trained from massive annotation data. Due to the difference of infrared and visible images, hand craft-based methods cannot achieve excellent performance for infrared and visible images [17]. Due to the lack of training data, the deep learning-based method can hardly be trained for infrared and visible images. The extraction and description of common feature points are the basis of the infrared and visible image match. The infrared and visible image match faces two issues as follows:  
(1) _Common feature point extraction_. Due to the difference of pixel intensities and local details like textures between infrared and visible images, some feature points do not have corresponding points in another image [10; 12]. Matching of these non-common feature points will only induce the wrong matches. (2) _Common feature point description_. Due to the difference of pixel intensities and local details between infrared and visible images, local descriptions of common feature points may be dissimilar [10; 11]. Matching of these dissimilar common feature points will induce the wrong matches. Extraction of common feature points plays a critical role in the infrared and visible image match [10; 17]. Non-common feature points will cause the low repeatability of detected features. In this case, no matter how well the feature description method is designed, a large amount of wrong matches will be brought in. Most of the existing infrared and visible image matching methods utilize the feature point detection methods of single-modality image matching methods [17]. However, these feature point detection methods cannot extract common feature points effectively for infrared and visible images [10; 17]. Although phase congruency has been used to extract feature points, phase congruency aims to solve nonlinear intensity variations of multi-modality images [18; 19]. Besides the difference of pixel intensities between infrared and visible images, the differences of local details such as textures will also bring in non-common feature points. The extraction of common feature points needs to fully consider the similarity of global structures of infrared and visible images.