An advanced gradient texture feature descriptor based on phase information for infrared and visible image matching

Abstract

Infrared and visible image matching has many applications in remote sensing, computer vision, military fields, etc. The differences in the many characteristics of infrared images and visible images make a robust feature description vital but difficult. Texture orientation information retains the general properties in refrared and visible images, and multi-scale, and multi-oriented Gabor filters can accurately reveal the texture orientation information. This paper presents a feature descriptor by capturing the phase information between neighboring pixels with Log-Gabor filters. Firstly, the original matching image is enhanced via histogram equalization to emphasize the regions of interest, and the gradient magnitude for each pixel is computed to extract the image profile, which advances the performance of the algorithm significantly. Secondly, multi-scale and multi-oriented Log–Gabor filters are utilized to obtain the angle information for different scales and phases in the neighboring region of each pixel, and the angle information is indexed by computing the maximum of energy, including the magnitude, real part, and imaginary part to generate the marked image in which the histograms of the subregion of the detected keypoints are employed to generate the feature descriptors. Finally, we advocate five evaluation measures for testing the performance of the algorithm. The proposed approach is evaluated with four data sets composed of images obtained in visible light and infrared spectra, and its performance is compared with the performance of the state-of-the-art algorithms: Scale-invariant feature transform(SIFT), Speeded up robust features(SURF), Oriented fast and rotated BRIEF(ORB), the edge-oriented histogram descriptor (EHD), the phase congruency edge-oriented histogram discriptor (PCEHD), and the Log–Gabor histogram descriptor (LGHD). The experimental results indicate that the performance of the proposed approach is higher than that of other state-of-the-art algorithms.