[1] Dogra A, Goyal B, Agrawal S. From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications[J]. IEEE Access, 2017, 5: 16040-16067.

[2] 陈震, 杨小平, 张聪炫, 等. 基于补偿机制的NSCT域红外与可见光图像融合[J]. 仪器仪表学报, 2016, 37(4): 860-870.

    Chen Z, Yang X P, Zhang C X, et al. Infrared and visible image fusion based on the compensation mechanism in NSCT domain[J]. Chinese Journal of Scientific Instrument, 2016, 37(4): 860-870.

[3] Zhang B H, Lu X Q, Pei H Q, et al. A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled shearlet transform[J]. Infrared Physics & Technology, 2015, 73: 286-297.

[4] 吴冬鹏, 毕笃彦, 何林远, 等. 基于NSSCT的红外与可见光图像融合[J]. 光学学报, 2017, 37(7): 0710003.

    Wu D P, Bi D Y, He L Y, et al. A fusion algorithm of infrared and visible image based on NSSCT[J]. Acta Optica Sinica, 2017, 37(7): 0710003.

[5] Yin M, Duan P H, Liu W, et al. A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation[J]. Neurocomputing, 2017, 226: 182-191.

[6] 邓辉, 王长龙, 胡永江, 等. 基于非下采样双树复轮廓波与自适应分块的红外与可见光图像融合[J]. 光子学报, 2019, 48(7): 0710006.

    Deng H, Wang C L, Hu Y J, et al. Fusion of infrared and visible images based on non-subsampled dual-tree complex contourlet and adaptive block[J]. Acta Photonica Sinica, 2019, 48(7): 0710006.

[7] Meher B, Agrawal S, Panda R, et al. A survey on region based image fusion methods[J]. Information Fusion, 2019, 48: 119-132.

[8] 朱浩然, 刘云清, 张文颖. 基于迭代导向滤波与多视觉权重信息的红外与可见光图像融合[J]. 光子学报, 2019, 48(3): 0310002.

    Zhu H R, Liu Y Q, Zhang W Y. Infrared and visible image fusion based on iterative guided filtering and multi-visual weight information[J]. Acta Photonica Sinica, 2019, 48(3): 0310002.

[9] Lu X Q, Zhang B H, Zhao Y, et al. The infrared and visible image fusion algorithm based on target separation and sparse representation[J]. Infrared Physics & Technology, 2014, 67: 397-407.

[10] LindbladT, Kinser JM. 脉冲耦合神经网络图像处理[M]. 马义德, 绽琨, 王兆滨, 等, 译. 北京: 高等教育出版社, 2008: 10- 20.

    LindbladT, Kinser JM. Image processing using pulse-coupled neural networks[M]. Ma Y D, Zhan K, Wang Z B, et al., Transl. Beijing: Higher Education Press, 2008: 10- 20.

[11] 马义德, 戴若兰, 李廉. 一种基于脉冲耦合神经网络和图像熵的自动图像分割方法[J]. 通信学报, 2002, 23(1): 46-51.

    Ma Y D, Dai R L, Li L. Automated image segmentation using pulse coupled neural networks and image's entropy[J]. Journal of China Institute of Communications, 2002, 23(1): 46-51.

[12] 邓翔宇, 马义德. PCNN参数自适应设定及其模型的改进[J]. 电子学报, 2012, 40(5): 955-964.

    Deng X Y, Ma Y D. PCNN model automatic parameters determination and its modified model[J]. Acta Electronica Sinica, 2012, 40(5): 955-964.

[13] 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.

[14] Li S T, Kang X D, Hu J W. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875.

[15] Zhang Y, Zhang L J, Bai X Z, et al. Infrared and visual image fusion through infrared feature extraction and visual information preservation[J]. Infrared Physics & Technology, 2017, 83: 227-237.

[16] 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.