[1] [1] MA J Y, ZHANG H, SHAO Z F, et al. GANMC: A generative adversarial network with multi classification constraints for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation, 2021, 70: 5005014.

[3] [3] ZHANG Z Y, ZHANG Y M, CAO Y F, et al. Infrared and visible airborne targets image fusion with applications to sense and avoid[J]. IFAC Papers On Line, 2020, 53(2): 14742-14747.

[4] [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] [5] JIN H Y, WANG Y Y. A fusion method for visible and infrared images based on contrast pyramid with teaching learning based optimization[J]. Infrared Physics & Technology, 2014, 64: 134-142.

[6] [6] LIU X H,CHEN Z B,QIN M Z. Infrared and visible image fusion using guided filter and convolutional sparse representation[J]. Opt. Precision Eng, 2018, 26(5): 1242-1253.

[8] [8] LIU Y, MIAO C Y, JI J H, et al. MMF: a multi scale MobileNet based fusion method for infrared and visible image[J]. Infrared Physics & Technology, 2021, 119: 103894.

[10] [10] 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.

[11] [11] ZHAN K, KONG L G, LIU B, et al. Multimodal image seamless fusion[J]. Journal of electronic imaging, 2019, 28(2): 023027.

[12] [12] JIAN L H, YANG X M, ZHOU Z L, et al. Multi scale image Fusion through rolling guidance filter[J]. Future Generation Computer Systems, 2018, 83(6): 310-325.

[13] [13] KONG W W, CHEN Y P, LEI Y. Medical image fusion using guided filter random walks and spatial frequency in framelet domain[J]. Signal Processing, 2021, 181: 107921.

[14] [14] FENG Y C, WU J, HU X H, et al. Medical image fusion using bilateral texture filtering[J]. Biomedical Signal Processing and Control, 2023, 85: 105004.

[15] [15] ZHANG Y G, ZHAO P, MA Y Z, et al. Multi-focus image fusion with joint guided image filtering[J]. Signal Processing: Image Communication, 2021, 92: 35-46.

[18] [18] TOET A. TNO image fusion dataset［DB/OL］.(2014).https//figs ha re.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029.

[19] [19] LIU Y, CHEN X, RABAB K, et al. Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters, 2019, 26(3): 485-489.

[20] [20] 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.

[21] [21] 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, 2017, 82: 8-13.

[22] [22] LIU F, SHEN T S, MA X X. Image fusion of cross bilateral filtering and visual weight information[J]. Chinese Journal of Scientific Instrument, 2017, 38(4): 1005-1013.

[23] [23] TANG L F, YUAN J T, ZHANG H, et al. PIA fusion: a progressive infrared and visible image fusion network based on illumination aware[J]. Information Fusion, 2022, 83: 79-92.

[24] [24] LI H, WU X J, JOSEF K. RFN-Nest: An end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 2021, 73: 72-86.

[25] [25] ZHANG H, MA J Y. SDNet: A versatile squeeze and decomposition network for real-time image fusion[J]. International Journal of Computer Vision, 2021, 129: 2761-2785.