[1] ZHANG Xingchen, DEMIRIS Y. Visible and infrared image fusion using deep learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, 1-20(2023).

[2] WANG Zhishe, SHAO Wenyu, YANG Fengbao et al. Infrared and visible image fusion method via interactive attention-based generative adversarial network[J]. Acta Photonica Sinica, 51, 0410002(2022).

[3] HAO Shuai, HE Tian, AN Beiyi et al. VDFEFuse: a novel fusion approach to infrared and visible images[J]. Infrared Physics & Technology, 121, 104048(2022).

[4] XIAO Wanxin, ZHANG Yafei, WANG Hongbin et al. Heterogeneous knowledge distillation for simultaneous infrared-visible image fusion and super-resolution[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5004015(2022).

[5] TU Zhengzheng, LI Zhun, LI Chenglong et al. Multi-interactive dual-decoder for RGB-thermal salient object detection[J]. IEEE Transactions on Image Processing, 30, 5678-5691(2021).

[6] ZHANG Xingchen, YE Ping, LEUNG H et al. Object fusion tracking based on visible and infrared images: a comprehensive review[J]. Information Fusion, 63, 166-187(2020).

[7] NAGARANI N, VENKATAKRISHNAN P, BALAJI N. Unmanned aerial vehicle's runway landing system with efficient target detection by using morphological fusion for military surveillance system[J]. Computer Communications, 151, 463-472(2020).

[8] GAO Yuan, MA Shiwei, LIU Jingjing et al. Fusion of medical images based on salient features extraction by PSO optimized fuzzy logic in NSST domain[J]. Biomedical Signal Processing and Control, 69, 102852(2021).

[9] CHEN Jun, LI Xuejiao, LUO Linbo et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition[J]. Information Sciences, 508, 64-78(2020).

[10] LU Xiaoqi, ZHANG Baohua, ZHAO Ying et al. The infrared and visible image fusion algorithm based on target separation and sparse representation[J]. Infrared Physics & Technology, 67, 397-407(2014).

[11] LI Hui, WU Xiaojun. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2018).

[12] LIU Yu, CHEN Xun, CHENG Juan et al. Infrared and visible image fusion with convolutional neural networks[J]. International Journal of Wavelets, Multiresolution and Information Processing, 16, 1850018(2018).

[13] JIAN Lihua, YANG Xiaomin, LIU Zheng et al. SEDRFuse: a symmetric encoder-decoder with residual block network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 1-15(2020).

[14] MA Jiayi, YU Wei, LIANG Pengwei et al. FusionGAN: a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 48, 11-26(2019).

[15] GOODFELLOW I, POUGET-ABADIE J, MIRZA M. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 3, 2672-2680(2014).

[16] MA Jiayi, XU Han, JIANG Junjun et al. DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion[J]. IEEE Transactions on Image Processing, 29, 4980-4995(2020).

[17] BRAIK M. Chameleon swarm algorithm: a bioinspired optimizer for solving engineering design problems[J]. Expert Systems with Applications, 174, 114685(2021).

[18] LIU Guangcan, LIN Zhouchen, YU Yong. Robust subspace segmentation by low-rank representation[C], 663-670(2010).

[19] LIU Guangcan, YAN Shuicheng. Latent low-rank representation for subspace segmentation and feature extraction[C], 1615-1622(2011).

[20] CUI Guangmang, FENG Huajun, XU Zhihai et al. Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition[J]. Optics Communications, 341, 199-209(2015).

[21] MA Jiayi, MA Yong, LI Chang. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 45, 153-178(2019).

[22] HUANG Gao, LIU Zhuang, VAN D et al. Densely connected convolutional networks[C], 4700-4708(2017).

[23] WANG Zhishe, WANG Junyao, WU Yuanyuan et al. UNFusion: a unified multi-scale densely connected network for infrared and visible image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 3360-3374(2021).

[25] LI Hui, WU Xiaojun, DURRANI T. Infrared and visible image fusion with ResNet and zerophase component analysis[J]. Infrared Physics & Technology, 102, 103039(2019).

[26] LI Hui, WU Xiaojun, KITTLER J. MDLatLRR: a novel decompo-sition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing, 29, 4733-4746(2020).

[27] ZHANG Hao, XU Han, XIAO Yang et al. Rethinking the image fusion: a fast unified image fusion network based on proportional maintenance of gradient and intensity[C], 34, 12797-12804(2020).

[28] LI Hui, WU Xiaojun, KITTLER J. RFN-Nest: an end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 73, 72-86(2021).

[29] ZHANG Xiaoli, LI Xiongfei, LI Jun. Validation and correlation analysis of metrics for evaluatiing performance of image fusion[J]. Acta Automatica Sinica, 40, 306-315(2014).