Infrared and visible image registration based on SIFT and sparse representation
Jianfang Dou, Qin Qin, Zimei Tu, Xishuai Peng, Yuanxiang Li · 2016
This paper proposes a visual-infrared image registration method based on sparse representation using Scale Invariant Features Transform (SIFT) features. Firstly, obtain the inverse image of the infrared image, enhance the visible and infrared image through Brightness Preserving Bi-Histogram Equalization (BPHE), and extract SIFT feature points and descriptors. Then, the SIFT descriptors of the visible image is sparse represented by the descriptors of the infrared images, find the initial matches based on l1 minimization. RANSAC is adopted to filter out the mismatches. Finally, optimize the transform parameters based on an improved Powell algorithm. Experimental results show the proposed method improves the registration performance compared to sift based methods.