[1] 刘帅奇, 郑伟, 赵杰, 等. 数字图像融合算法分析与应用[M]. 北京: 机械工业出版社, 2018: 111- 137.

    Liu SQ, ZhengW, ZhaoJ, et al.Analysis and application of algorithm for digital image fusion[M]. Beijing: China Machine Press, 2018: 111- 137.

[2] 王昕, 吉桐伯, 刘富. 结合目标提取和压缩感知的红外与可见光图像融合[J]. 光学精密工程, 2016, 24(7): 1743-1753.

    Wang X, Ji T B, Liu F. Fusion of infrared and visible images based on target segmentation and compressed sensing[J]. Optics and Precision Engineering, 2016, 24(7): 1743-1753.

[3] Liu C H, Qi Y, Ding W R. Infrared and visible image fusion method based on saliency detection in sparse domain[J]. Infrared Physics & Technology, 2017, 83: 94-102.

[4] Shahdoosti H R, Ghassemian H. Combining the spectral PCA and spatial PCA fusion methods by an optimal filter[J]. Information Fusion, 2016, 27: 150-160.

[5] Ma J Y, Chen C, Li C, et al. Infrared and visible image fusion via gradient transfer and total variation minimization[J]. Information Fusion, 2016, 31: 100-109.

[6] 吴一全, 王志来. 基于目标提取与引导滤波增强的红外与可见光图像融合[J]. 光学学报, 2017, 37(8): 0810001.

    Wu Y Q, Wang Z L. Infrared and visible image fusion based on target extraction and guided filtering enhancement[J]. Acta Optica Sinica, 2017, 37(8): 0810001.

[7] 陈木生. 结合NSCT和压缩感知的红外与可见光图像融合[J]. 中国图象图形学报, 2016, 21(1): 39-44.

    Chen M S. Image fusion of visual and infrared image based on NSCT and compressed sensing[J]. Journal of Image and Graphics, 2016, 21(1): 39-44.

[8] 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.

[9] 江泽涛, 吴辉, 周哓玲. 基于改进引导滤波和双通道脉冲发放皮层模型的红外与可见光图像融合算法[J]. 光学学报, 2018, 38(2): 0210002.

    Jiang Z T, Wu H, Zhou X L. Infrared and visible image fusion algorithm based on improved guided filtering and dual-channel spiking cortical model[J]. Acta Optica Sinica, 2018, 38(2): 0210002.

[10] Cai J J, Cheng Q M, Peng M J, et al. Fusion of infrared and visible images based on nonsubsampled contourlet transform and sparse K-SVD dictionary learning[J]. Infrared Physics & Technology, 2017, 82: 85-95.

[11] 蔡怀宇, 卓励然, 朱攀, 等. 基于非下采样轮廓波变换和直觉模糊集的红外与可见光图像融合[J]. 光子学报, 2018, 47(6): 0610002.

    Cai H Y, Zhuo L R, Zhu P, et al. Fusion of infrared and visible images based on non-subsampled contourlet transform and intuitionistic fuzzy set[J]. Acta Photonica Sinica, 2018, 47(6): 0610002.

[12] 周渝人, 耿爱辉, 张强, 等. 基于压缩感知的红外与可见光图像融合[J]. 光学精密工程, 2015, 23(3): 855-863.

    Zhou Y R, Geng A H, Zhang Q, et al. Fusion of infrared and visible images based on compressive sensing[J]. Optics and Precision Engineering, 2015, 23(3): 855-863.

[13] 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.

[14] Chen Y, Qin Z. Gradient-based compressive image fusion[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(3): 227-237.

[15] Zhang Q, Maldague X. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing[J]. Infrared Physics & Technology, 2016, 74: 11-20.

[16] Wang Z Z, Deller J R, Fleet B D. Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis[J]. Journal of Electronic Imaging, 2016, 25(1): 013007.

[17] Fu Z Z, Wang X, Xu J, et al. Infrared and visible images fusion based on RPCA and NSCT[J]. Infrared Physics & Technology, 2016, 77: 114-123.

[18] Li J, Song M H, Peng Y X. Infrared and visible image fusion based on robust principal component analysis and compressed sensing[J]. Infrared Physics & Technology, 2018, 89: 129-139.

[19] Do T T, Gan L, Nguyen N H, et al. Fast and efficient compressive sensing using structurally random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(1): 139-154.

[20] Fu ZZ, Dai XD, LiY, et al. An improved visible and infrared image fusion based on local energy and fuzzy logic[C]∥2014 12th International Conference on Signal Processing (ICSP), October 19-23, 2014, Hangzhou, Zhejiang, China. New York: IEEE, 2014: 861- 865.