[1] ZHANG H, XU H, TIAN X et al. Image fusion meets deep learning： a survey and perspective[J]. Information Fusion, 76, 323-336(2021).

[2] MA X, YU CH Y, TONG Y X et al. Infrared image and visible image fusion algorithm based on secondary image decomposition[J]. Opt. Precision Eng., 32, 1567-1581(2024).

     马鑫, 喻春雨, 童亦新. 基于二次图像分解的红外图像与可见光图像融合[J]. 光学 精密工程, 32, 1567-1581(2024).

[3] TAHARA T. Adding dimensions with Lucy–Richardson-Rosen algorithm to incoherent imaging[J]. Opto-Electronic Advances, 6, 230047(2023).

[4] FANG A Q, LI Y. Dynamic and static fusion mechanisms of infrared and visible images[J]. Pattern Recognition, 155, 110689(2024).

[5] TANG L, ZHANG H, XU H et al. Deep learning-based image fusion： A survey[J]. Journal of Image and Graphics, 28, 3-36(2023).

[6] LONG ZH L, DENG Y M, XIE J et al. Infrared and visible images fusion based on improved multi-scale structural fusion[J]. Opt. Precision Eng., 32, 1101-1110(2024).

     龙志亮, 邓月明, 谢竞. 改进多尺度结构化融合的红外与可见光图像融合[J]. 光学 精密工程, 32, 1101-1110(2024).

[7] 徐少平, 周常飞, 肖建. 基于预训练固定参数和深度特征调制的红外与可见光图像融合网络[J]. 电子与信息学报, 46, 3305-3313(2024).

     XU SH P, ZHOU CH F, XIAO J et al. A fusion network for infrared and visible images based on pre-trained fixed parameters and deep feature modulation[J]. Journal of Electronics & Information Technology, 46, 3305-3313(2024).

[8] LONG Z L, DENG Y M, XIE J et al. Infrared and visible images fusion based on improved multi-scale structural fusion[J]. Opt. Precision Eng., 32, 1101-1110(2024).

     龙志亮, 邓月明, 谢竞. 改进多尺度结构化融合的红外与可见光图像融合[J]. 光学 精密工程, 32, 1101-1110(2024).

[9] WANG Z G, ZHAN J, LI Y et al. A new scheme of vehicle detection for severe weather based on multi-sensor fusion[J]. Measurement, 191, 110737(2022).

[10] SUN L, LI Y H, MUHAMMAD G. Soft computing-driven infrared and visible image fusion network for security application service[J]. Applied Soft Computing, 165, 112114(2024).

[11] 郑海君, 葛斌, 夏晨星. 多特征聚合的红外-可见光行人重识别[J]. 光电工程, 50, 110-123(2023).

     ZHENG H J, GE B, XIA C X et al. Infrared-visible person re-identification based on multi feature aggregation[J]. Opto-Electronic Engineering, 50, 110-123(2023).

[12] LUO X, ZHU H, ZHANG Z L. IR-YOLO： real-time infrared vehicle and pedestrian detection[J]. Computers, 78, 2667-2687(2024).

[13] LIU Y P, JIN J, WANG Q et al. Region level based multi-focus image fusion using quaternion wavelet and normalized cut[J]. Signal Processing, 97, 9-30(2014).

[14] LIU X B, MEI W B, DU H Q. Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion[J]. Neurocomputing, 235, 131-139(2017).

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

[16] CHEN J, LI X J, LUO L B et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition[J]. Information Sciences, 508, 64-78(2020).

[17] LI H, WU X J, KITTLER J. MDLatLRR： a novel decomposition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing(2020).

[18] LIU Y, CHEN X, WARD R K et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 23, 1882-1886(2016).

[19] CVEJIC N, BULL D, CANAGARAJAH N. Region-based multimodal image fusion using ICA bases[J]. IEEE Sensors Journal, 7, 743-751(2007).

[20] MOU J, GAO W, SONG Z X. Image fusion based on non-negative matrix factorization and infrared feature extraction[C], 16, 1046-1050(2013).

[21] FU Z Z, WANG X, XU J et al. Infrared and visible images fusion based on RPCA and NSCT[J]. Infrared Physics & Technology, 77, 114-123(2016).

[22] 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, 83, 94-102(2017).

[23] MA J L, ZHOU Z Q, WANG B et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 82, 8-17(2017).

[24] WU S T, YANG Z C, MA C G et al. Deep learning enhanced NIR-II volumetric imaging of whole mice vasculature[J]. Opto-Electronic Advances, 6, 220105(2023).

[25] 马鑫, 喻春雨, 童亦新. 基于二次图像分解的红外图像与可见光图像融合[J]. 光学 精密工程, 32, 1567-1581(2024).

     MA X, YU CH Y, TONG Y X et al. Infrared image and visible image fusion algorithm based on secondary image decomposition[J]. Opt. Precision Eng., 32, 1567-1581(2024).

[26] LU Y X, ZHANG W J, ZHAO D W et al. PTPFusion： a progressive infrared and visible image fusion network based on texture preserving[J]. Image and Vision Computing, 151, 105287(2024).

[27] LU G S, FANG Z L, TIAN J J et al. GAN-HA： a generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion[J]. Infrared Physics & Technology, 142, 105548(2024).

[28] LONG Y Z, JIA H T, ZHONG Y D et al. RXDNFuse： a aggregated residual dense network for infrared and visible image fusion[J]. Information Fusion, 69, 128-141(2021).

[29] ZHANG H, XU H, XIAO Y et al. Rethinking the image fusion： a fast unified image fusion network based on proportional maintenance of gradient and intensity[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12797-12804(2020).

[30] YANG Y CH, GAO X Y, DANG J W et al. Infrared and visible image fusion based on WEMD and generative adversarial network reconstruction[J]. Opt. Precision Eng., 30, 320-330(2022).

     杨艳春, 高晓宇, 党建武. 基于WEMD和生成对抗网络重建的红外与可见光图像融合[J]. 光学 精密工程, 30, 320-330(2022).

[31] LI H, WU X J. DenseFuse： a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2019).

[32] ZHANG Y, LIU Y, SUN P et al. IFCNN： a general image fusion framework based on convolutional neural network[J]. Information Fusion, 54, 99-118(2020).

[33] LI H, XU T Y, WU X J et al. LRRNet： a novel representation learning guided fusion network for infrared and visible images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 11040-11052(2023).

[34] ZHANG H, MA J Y. SDNet： a versatile squeeze-and-decomposition network for real-time image fusion[J]. International Journal of Computer Vision, 129, 2761-2785(2021).

[35] TANG L F, YUAN J T, ZHANG H et al. PIAFusion： a progressive infrared and visible image fusion network based on illumination aware[J]. Information Fusion, 83, 79-92(2022).

[36] QI Y J, HOU Q H. Infrared and visible image fusion combining multi-scale and convolutional attention[J]. Infrared Technology, 46, 1060-1069(2024).

     祁艳杰, 侯钦河. 多尺度和卷积注意力相结合的红外与可见光图像融合[J]. 红外技术, 46, 1060-1069(2024).

[37] MA J Y, YU W, LIANG P W et al. FusionGAN： a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 48, 11-26(2019).

[38] QU G H, ZHANG D L, YAN P F. Information measure for performance of image fusion[J]. Electronics Letters, 38, 313-315(2002).

[39] HAN Y, CAI Y Z, CAO Y et al. A new image fusion performance metric based on visual information fidelity[J]. Information Fusion, 14, 127-135(2013).