[1] 丁文杉, 毕笃彦, 何林远, 等. 基于剪切波变换和邻域结构特征的红外与可见光图像融合[J]. 光学学报, 2017, 37(10): 1010002.

    Ding W S, Bi D Y, He L Y, et al. Fusion of infrared and visible images based on shearlet transform and neighborhood structure features[J]. Acta Optica Sinica, 2017, 37(10): 1010002.

[2] 刘先红, 陈志斌. 基于多尺度方向引导滤波和卷积稀疏表示的红外与可见光图像融合[J]. 光学学报, 2017, 37(11): 1110004.

    Liu X H, Chen Z B. Fusion of infrared and visible images based on multi-scale directional guided filter and convolutional sparse representation[J]. Acta Optica Sinica, 2017, 37(11): 1110004.

[3] Pal AR, SinghaA. A comparative analysis of visual and thermal face image fusion based on different wavelet family[C]//2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), April 6-7, 2017, Shillong, India. New York: IEEE, 2017: 8071895.

[4] Ma J Y, Ma Y, Li C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 2019, 45: 153-178.

[5] 刘晓春, 钟涛, 于起峰, 等. 基于局部频率信息和单纯型-模拟退火的异源图像配准[J]. 光学学报, 2013, 33(6): 0615002.

    Liu X C, Zhong T, Yu Q F, et al. Multi-modal image registration based on local frequency information using modified simplex-simulated annealing algorithm[J]. Acta Optica Sinica, 2013, 33(6): 0615002.

[6] 林青, 金伟其, 郭宏, 等. 可见光/长波红外共聚焦窗口望远物镜设计[J]. 光学学报, 2012, 32(9): 0922005.

    Lin Q, Jin W Q, Guo H, et al. Confocal-window telescope objective design in visible and long-wave infrared[J]. Acta Optica Sinica, 2012, 32(9): 0922005.

[7] Kong S G, Heo J, Boughorbel F, et al. Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition[J]. International Journal of Computer Vision, 2007, 71(2): 215-233.

[8] Boughorbel F, Mercimek M, Koschan A, et al. A new method for the registration of three-dimensional point-sets: the Gaussian fields framework[J]. Image and Vision Computing, 2010, 28(1): 124-137.

[9] Myronenko A, Song X B. Point set registration: coherent point drift[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12): 2262-2275.

[10] Ma J Y, Zhao J, Ma Y, et al. Non-rigid visible and infrared face registration via regularized Gaussian fields criterion[J]. Pattern Recognition, 2015, 48(3): 772-784.

[11] Tian T, Mei X G, Yu Y, et al. Automatic visible and infrared face registration based on silhouette matching and robust transformation estimation[J]. Infrared Physics & Technology, 2015, 69: 145-154.

[12] Gerogiannis D, Nikou C, Likas A. The mixtures of student's t-distributions as a robust framework for rigid registration[J]. Image and Vision Computing, 2009, 27(9): 1285-1294.

[13] Zhou Z Y, Zheng J, Dai Y K, et al. Robust non-rigid point set registration using student's-t mixture model[J]. PLoS One, 2014, 9(3): e91381.

[14] Maiseli B, Gu Y F, Gao H J. Recent developments and trends in point set registration methods[J]. Journal of Visual Communication and Image Representation, 2017, 46: 95-106.

[15] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522.

[16] Ling H B, Jacobs D W. Shape classification using the inner-distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 286-299.

[17] Peel D. McLachlan G J. Robust mixture modelling using the t distribution[J]. Statistics and Computing, 2000, 10(4): 339-348.

[18] Chui H L, Rangarajan A. A new point matching algorithm for non-rigid registration[J]. Computer Vision and Image Understanding, 2003, 89(2/3): 114-141.

[19] Zhan L C, Zhuang Y, Huang L D. Infrared and visible images fusion method based on discrete wavelet transform[J]. Journal of Computers, 2017, 28(2): 57-71.