论文信息 - Infrared and visible image registration based on SIFT and sparse representation

Infrared and visible image registration based on SIFT and sparse representation

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.

[1] David G. Lowe,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2] Qian Chen,et al. Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[3] Jan Flusser,et al. Image registration methods: a survey , 2003, Image Vis. Comput..

[4] Yeong-Taeg Kim,et al. Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[5] Lisa M. Brown,et al. A survey of image registration techniques , 1992, CSUR.

[6] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.