[1] Ma J Y, Ma Y, Li C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 45, 153-178(2019).

[2] Zhang X C, Demiris Y. Visible and infrared image fusion using deep learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 10535-10554(2023).

[3] Karim S, Tong G, Li J Y et al. Current advances and future perspectives of image fusion: a comprehensive review[J]. Information Fusion, 90, 185-217(2023).

[4] Wang E L, Li J W, Lei J et al. Deep learning-based infrared and visible image fusion: a survey[J]. Journal of Frontiers of Computer Science and Technology, 18, 899-915(2024).

[5] Zhang D D, Wang C P, Fu Q. Overview of infrared and visible image fusion algorithms based on deep learning framework[J]. Laser & Infrared, 52, 1288-1298(2022).

[6] Li L, Wang H M, Li C K. A review of deep learning fusion methods for infrared and visible images[J]. Infrared and Laser Engineering, 51, 20220125(2022).

[7] Burt P, Adelson E. The Laplacian pyramid as a compact image code[J]. IEEE Transactions on Communications, 31, 532-540(1983).

[8] Bulanon D M, Burks T F, Alchanatis V. Image fusion of visible and thermal images for fruit detection[J]. Biosystems Engineering, 103, 12-22(2009).

[9] Vanmali A V, Gadre V M. Visible and NIR image fusion using weight-map-guided Laplacian-Gaussian pyramid for improving scene visibility[J]. Sādhanā, 42, 1063-1082(2017).

[10] Yu X L, Ren J L, Chen Q et al. A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space[J]. Optik, 125, 6010-6016(2014).

[11] 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 and Technology, 64, 134-142(2014).

[12] He D X, Meng Y, Wang C Y. Contrast pyramid based image fusion scheme for infrared image and visible image[C], 597-600(2011).

[13] Jin H Y, Jiao L C, Liu F et al. Fusion of infrared and visual images based on contrast pyramid directional filter banks using clonal selection optimizing[J]. Optical Engineering, 47, 027002(2008).

[14] Jin H Y, Xi Q, Wang Y Y et al. Fusion of visible and infrared images using multiobjective evolutionary algorithm based on decomposition[J]. Infrared Physics & Technology, 71, 151-158(2015).

[15] Liu Z, Tsukada K, Hanasaki K et al. Image fusion by using steerable pyramid[J]. Pattern Recognition Letters, 22, 929-939(2001).

[16] Liu G, Jing Z L, Sun S Y et al. Image fusion based on expectation maximization algorithm and steerable pyramid[J]. Chinese Optics Letters, 2, 386-389(2004).

[17] Deng H B, Ma Y D. Image fusion based on steerable pyramid and PCNN[C], 569-573(2009).

[18] Zhan L C, Yi Z, Huang L D. Infrared and visible images fusion method based on discrete wavelet transform[J]. Journal of Computers, 28, 57-71(2017).

[19] Singh S, Singh H, Gehlot A et al. IR and visible image fusion using DWT and bilateral filter[J]. Microsystem Technologies, 29, 457-467(2023).

[20] Chipman L J, Orr T M, Graham L N. Wavelets and image fusion[C], 248-251(2002).

[21] Niu Y F, Xu S T, Wu L Z et al. Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform[J]. Mathematical Problems in Engineering, 2012, 275138(2012).

[22] Aghamaleki J A, Ghorbani A. Image fusion using dual tree discrete wavelet transform and weights optimization[J]. The Visual Computer, 39, 1181-1191(2023).

[23] Zuo Y J, Liu J H, Bai G B et al. Airborne infrared and visible image fusion combined with region segmentation[J]. Sensors, 17, 1127-1142(2017).

[24] Jiang M, Sha G J, Li N. Infrared and visible image fusion with guided filtering and dual-tree complex wavelet transform[J]. Laser & Optoelectronics Progress, 60, 1010008(2023).

[25] Zou Y L, Liang X Q, Wang T M. Visible and infrared image fusion using the lifting wavelet[J]. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11, 6290-6295(2013).

[26] Ramesh C, Ranjith T. Fusion performance measures and a lifting wavelet transform based algorithm for image fusion[C], 317-320(2002).

[27] Wang X H, Shen Y T, Zhou Z G et al. An image fusion algorithm based on lifting wavelet transform[J]. Journal of Optics, 17, 055702(2015).

[28] Li H F, Qiu H M, Yu Z T et al. Infrared and visible image fusion scheme based on NSCT and low-level visual features[J]. Infrared Physics and Technology, 76, 174-184(2016).

[29] Adu J H, Gan J H, Wang Y et al. Image fusion based on nonsubsampled contourlet transform for infrared and visible light image[J]. Infrared Physics & Technology, 61, 94-100(2013).

[30] Yang B, Li S T, Sun F M. Image fusion using nonsubsampled contourlet transform[C], 719-724(2007).

[31] He K J, Zhou D M, Zhang X J et al. Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain[J]. Journal of Applied Remote Sensing, 11, 015011(2017).

[32] Wang M L, Wang X L, Zhang C S. Infrared and visible image fusion algorithm based on dynamic range compression enhancement and NSST[J]. Acta Photonica Sinica, 51, 0910002(2022).

[33] Hu P, Wang C J, Li D Q et al. An improved hybrid multiscale fusion algorithm based on NSST for infrared–visible images[J]. The Visual Computer, 40, 1245-1259(2024).

[34] 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, 73, 286-297(2015).

[35] Huang Y, Bi D Y, Wu D P. Infrared and visible image fusion based on different constraints in the non-subsampled shearlet transform domain[J]. Sensors, 18, 1169-1182(2018).

[36] Cui B. Infrared and visible images fusion based on gradient bilateral filtering[C], 891-895(2016).

[37] Hu J W, Li S T. The multiscale directional bilateral filter and its application to multisensor image fusion[J]. Information Fusion, 13, 196-206(2012).

[38] Xing C D, Wang Z S, Meng F L et al. Fusion of infrared and visible images with Gaussian smoothness and joint bilateral filtering iteration decomposition[J]. IET Computer Vision, 13, 44-52(2019).

[39] Shi Z F, Xu J T, Zhang Y et al. Fusion for visible and infrared images using visual weight analysis and bilateral filter-based multi scale decomposition[J]. Infrared Physics & Technology, 71, 363-369(2015).

[40] Wang C F, Wang Y S, Jia W J et al. Fusion of infrared and visible images based on fuzzy logic and guided filtering[C], 80-83(2020).

[41] Liang J M, Yang S, Tian L F. Infrared and visible image fusion based on image enhancement and rolling guidance filtering[J]. Laser & Optoelectronics Progress, 60, 0210006(2023).

[42] Gan W, Wu X H, Wu W et al. Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter[J]. Infrared Physics and Technology, 72, 37-51(2015).

[43] Ren L, Pan Z B, Cao J Z et al. Infrared and visible image fusion based on weighted variance guided filter and image contrast enhancement[J]. Infrared Physics and Technology, 114, 103662(2021).

[44] Liu Y, Liu S P, Wang Z F. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 24, 147-164(2015).

[45] Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit[J]. Information Fusion, 13, 10-19(2012).

[46] Wang J, Peng J Y, Feng X Y et al. Fusion method for infrared and visible images by using non-negative sparse representation[J]. Infrared Physics & Technology, 67, 477-489(2014).

[47] 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, 67, 397-407(2014).

[48] Yang Y, Zhang Y M, Huang S Y et al. Infrared and visible image fusion using visual saliency sparse representation and detail injection model[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5001715(2020).

[49] Yang B, Yang C, Huang G Y. Efficient image fusion with approximate sparse representation[J]. International Journal of Wavelets, Multiresolution and Information Processing, 14, 1650024(2016).

[50] Kim M, Han D K, Ko H. Joint patch clustering-based dictionary learning for multimodal image fusion[J]. Information Fusion, 27, 198-214(2016).

[51] Li X L, Tan H S, Zhou F Q et al. Infrared and visible image fusion based on domain transform filtering and sparse representation[J]. Infrared Physics & Technology, 131, 104701(2023).

[52] Aishwarya N, Bennila Thangammal C. An image fusion framework using novel dictionary based sparse representation[J]. Multimedia Tools and Applications, 76, 21869-21888(2017).

[53] Li S S, Zou Y H, Wang G J et al. Infrared and visible image fusion method based on principal component analysis network and multi-scale morphological gradient[J]. Infrared Physics & Technology, 133, 104810(2023).

[54] Patil U, Mudengudi U. Image fusion using hierarchical PCA[C](2011).

[55] 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 and Technology, 89, 129-139(2018).

[56] 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, 25, 013007(2016).

[57] Lu Y, Wang F X, Luo X Y et al. Novel infrared and visible image fusion method based on independent component analysis[J]. Frontiers of Computer Science, 8, 243-254(2014).

[58] Cvejic N, Bull D, Canagarajah N. Region-based multimodal image fusion using ICA bases[J]. IEEE Sensors Journal, 7, 743-751(2007).

[59] Liu Z W, Feng Y. Infrared and visible image fusion based on compressive sensing and OSS-ICA-bases[C], 1852-1856(2018).

[60] Mou J, Gao W, Song Z X. Image fusion based on non-negative matrix factorization and infrared feature extraction[C], 1046-1050(2013).

[61] Kong W W, Lei Y, Zhao H X. Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization[J]. Infrared Physics & Technology, 67, 161-172(2014).

[62] Zhang Y X, Chen L, Jia J et al. Multi-focus image fusion based on non-negative matrix factorization and difference images[J]. Signal Processing, 105, 84-97(2014).

[63] Cui G M, Feng H J, Xu Z H 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).

[64] Chen J, Wu K L, Cheng Z et al. A saliency-based multiscale approach for infrared and visible image fusion[J]. Signal Processing, 182, 107936(2021).

[65] 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 and Technology, 82, 8-17(2017).

[66] Liu Y C, Dong L L, Xu W H. Infrared and visible image fusion via salient object extraction and low-light region enhancement[J]. Infrared Physics and Technology, 124, 104223(2022).

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

[68] Liu W Q, Wang R H, Liu X W et al. Infrared and visible image fusion method based on saliency target extraction and poisson reconstruction[J]. Laser & Optoelectronics Progress, 60, 1610012(2023).

[69] Liu Y, Wang L, Li H F et al. Multi-focus image fusion with deep residual learning and focus property detection[J]. Information Fusion, 86, 1-16(2022).

[70] Li H, Wu X J. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2018).

[71] Li H, Wu X J, Durrani T. NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial/channel attention models[J]. IEEE Transactions on Instrumentation and Measurement, 69, 9645-9656(2020).

[72] Zhao Z X, Xu S, Zhang C X et al. DIDFuse: deep image decomposition for infrared and visible image fusion[C], 976(2021).

[73] Jian L H, Yang X M, Liu Z 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, 5002215(2020).

[74] Zhao F, Zhao W D, Yao L B et al. Self-supervised feature adaption for infrared and visible image fusion[J]. Information Fusion, 76, 189-203(2021).

[75] Xu H, Zhang H, Ma J Y. Classification saliency-based rule for visible and infrared image fusion[J]. IEEE Transactions on Computational Imaging, 7, 824-836(2021).

[76] Xu H, Wang X Y, Ma J Y. DRF: disentangled representation for visible and infrared image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5006713(2021).

[77] Li H, Wu X J, Kittler J. RFN-Nest: an end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 73, 72-86(2021).

[78] Wang Z S, Wang J Y, Wu Y Y 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(2022).

[79] Xu H, Gong M Q, Tian X et al. CUFD: an encoder-decoder network for visible and infrared image fusion based on common and unique feature decomposition[J]. Computer Vision and Image Understanding, 218, 103407(2022).

[80] Zhao Z X, Xu S, Zhang J S et al. Efficient and model-based infrared and visible image fusion via algorithm unrolling[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 1186-1196(2022).

[81] Wang Z S, Wu Y Y, Wang J Y et al. Res2Fusion: infrared and visible image fusion based on dense Res2net and double nonlocal attention models[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5005012(2022).

[82] Xiao W X, Zhang Y F, Wang H B et al. Heterogeneous knowledge distillation for simultaneous infrared-visible image fusion and super-resolution[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5004015(2022).

[83] Wang C W, Wu J S, Zhu Z X et al. MSFNet: MultiStage Fusion Network for infrared and visible image fusion[J]. Neurocomputing, 507, 26-39(2022).

[84] Zhu Z J, Yang X G, Lu R T et al. CLF-net: contrastive learning for infrared and visible image fusion network[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5021015(2022).

[85] Luo X Q, Gao Y H, Wang A Q et al. IFSepR: a general framework for image fusion based on separate representation learning[J]. IEEE Transactions on Multimedia, 25, 608-623(2021).

[86] Li B C, Lu J X, Liu Z F et al. AEFusion: a multi-scale fusion network combining Axial attention and Entropy feature Aggregation for infrared and visible images[J]. Applied Soft Computing, 132, 109857(2023).

[87] Tang L F, Xiang X Y, Zhang H et al. DIVFusion: Darkness-free infrared and visible image fusion[J]. Information Fusion, 91, 477-493(2023).

[88] Li X L, Li Y F, Chen H J et al. CCAFusion: cross-modal coordinate attention network for infrared and visible image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 34, 866-881(2024).

[89] Li G F, Qian X H, Qu X D. SOSMaskFuse: an infrared and visible image fusion architecture based on salient object segmentation mask[J]. IEEE Transactions on Intelligent Transportation Systems, 24, 10118-10137(2023).

[90] Liu Y, Chen X, Cheng J et al. Infrared and visible image fusion with convolutional neural networks[J]. International Journal of Wavelets, Multiresolution and Information Processing, 16, 1850018(2018).

[91] 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).

[92] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).

[93] Huang G, Liu Z, Van Der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[94] 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, 83/84, 79-92(2022).

[95] Jian L H, Rayhana R, Ma L et al. Infrared and visible image fusion based on deep decomposition network and saliency analysis[J]. IEEE Transactions on Multimedia, 24, 3314-3326(2021).

[96] An W B, Wang H M. Infrared and visible image fusion with supervised convolutional neural network[J]. Optik, 219, 165120(2020).

[97] 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 and Technology, 83, 227-237(2017).

[98] Zhu D P, Zhan W D, Jiang Y C et al. IPLF: a novel image pair learning fusion network for infrared and visible image[J]. IEEE Sensors Journal, 22, 8808-8817(2022).

[99] Ma J Y, Tang L F, Xu M L et al. STDFusionNet: an infrared and visible image fusion network based on salient target detection[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5009513(2021).

[100] Tang L F, Yuan J T, Ma J Y. Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network[J]. Information Fusion, 82, 28-42(2022).

[101] Li H, Wu X J, Durrani T S. Infrared and visible image fusion with ResNet and zero-phase component analysis[J]. Infrared Physics and Technology, 102, 103039(2019).

[102] Wang M, Liu X W, Jin H P. A generative image fusion approach based on supervised deep convolution network driven by weighted gradient flow[J]. Image and Vision Computing, 86, 1-16(2019).

[103] 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).

[104] Li Y, Wang J X, Miao Z et al. Unsupervised densely attention network for infrared and visible image fusion[J]. Multimedia Tools and Applications, 79, 34685-34696(2020).

[105] 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, 119, 103894(2021).

[106] Xu H, Ma J Y, Jiang J J et al. U2Fusion: a unified unsupervised image fusion network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 502-518(2022).

[107] Xu M L, Tang L F, Zhang H et al. Infrared and visible image fusion via parallel scene and texture learning[J]. Pattern Recognition, 132, 108929(2022).

[108] Lu Y X, Qiu Y, Gao Q W et al. Infrared and visible image fusion based on tight frame learning via VGG19 network[J]. Digital Signal Processing, 131, 103745(2022).

[109] Cheng C, Sun C, Sun Y Q et al. StyleFuse: an unsupervised network based on style loss function for infrared and visible image fusion[J]. Signal Processing: Image Communication, 106, 116722(2022).

[110] Cheng C Y, Xu T Y, Wu X J. MUFusion: a general unsupervised image fusion network based on memory unit[J]. Information Fusion, 92, 80-92(2023).

[111] Liu J Y, Dian R W, Li S T et al. SGFusion: a saliency guided deep-learning framework for pixel-level image fusion[J]. Information Fusion, 91, 205-214(2023).

[112] Wang J X, Xi X L, Li D M et al. FusionGRAM: an infrared and visible image fusion framework based on gradient residual and attention mechanism[J]. IEEE Transactions on Instrumentation and Measurement, 72, 5005412(2023).

[113] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C], 2672-2680(2014).

[114] 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).

[115] Ma J Y, Liang P W, Yu W et al. Infrared and visible image fusion via detail preserving adversarial learning[J]. Information Fusion, 54, 85-98(2020).

[116] Ma J Y, Zhang H, Shao Z F et al. GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5005014(2020).

[117] Fu Y, Wu X J, Durrani T. Image fusion based on generative adversarial network consistent with perception[J]. Information Fusion, 72, 110-125(2021).

[118] Le Z L, Huang J, Xu H et al. UIFGAN: an unsupervised continual-learning generative adversarial network for unified image fusion[J]. Information Fusion, 88, 305-318(2022).

[119] Rao Y J, Wu D, Han M N et al. AT-GAN: a generative adversarial network with attention and transition for infrared and visible image fusion[J]. Information Fusion, 92, 336-349(2023).

[120] Ma J Y, Xu H, Jiang J J 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).

[121] Li J, Huo H T, Li C et al. AttentionFGAN: infrared and visible image fusion using attention-based generative adversarial networks[J]. IEEE Transactions on Multimedia, 23, 1383-1396(2020).

[122] Wang Z S, Shao W Y, Chen Y L et al. Infrared and visible image fusion via interactive compensatory attention adversarial learning[J]. IEEE Transactions on Multimedia, 25, 7800-7813(2022).

[123] Zhou H B, Wu W, Zhang Y D et al. Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network[J]. IEEE Transactions on Multimedia, 25, 635-648(2021).

[124] Song A Y, Duan H X, Pei H D et al. Triple-discriminator generative adversarial network for infrared and visible image fusion[J]. Neurocomputing, 483, 183-194(2022).

[125] Zhao Y Q, Fu G Y, Wang H Q et al. The fusion of unmatched infrared and visible images based on generative adversarial networks[J]. Mathematical Problems in Engineering, 2020, 3739040(2020).

[126] Li J, Huo H T, Liu K J et al. Infrared and visible image fusion using dual discriminators generative adversarial networks with Wasserstein distance[J]. Information Sciences, 529, 28-41(2020).

[127] Li J, Huo H T, Li C et al. Multigrained attention network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 70, 5002412(2020).

[128] Liu J Y, Fan X, Jiang J et al. Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 105-119(2022).

[129] Zhou H B, Hou J L, Zhang Y D et al. Unified gradient- and intensity-discriminator generative adversarial network for image fusion[J]. Information Fusion, 88, 184-201(2022).

[130] Liu J Y, Fan X, Huang Z B et al. Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection[C], 5792-5801(2022).

[131] Gao Y, Ma S W, Liu J J. DCDR-GAN: a densely connected disentangled representation generative adversarial network for infrared and visible image fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 549-561(2023).

[132] Wang Z S, Shao W Y, Chen Y L et al. A cross-scale iterative attentional adversarial fusion network for infrared and visible images[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 3677-3688(2023).

[133] Tang Z M, Xiao G B, Guo J W et al. Dual-attention-based feature aggregation network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 72, 5010013(2023).

[134] Zhao H B, Nie R C. DNDT: infrared and visible image fusion via DenseNet and dual-transformer[C], 71-75(2021).

[135] Tang W, He F Z, Liu Y. YDTR: infrared and visible image fusion via Y-shape dynamic transformer[J]. IEEE Transactions on Multimedia, 25, 5413-5428(2022).

[136] Tang W, He F Z, Liu Y. TCCFusion: an infrared and visible image fusion method based on transformer and cross correlation[J]. Pattern Recognition, 137, 109295(2023).

[137] Tang W, He F Z, Liu Y et al. DATFuse: infrared and visible image fusion via dual attention transformer[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 3159-3172(2023).

[138] Chang Z H, Feng Z X, Yang S Y et al. AFT: adaptive fusion transformer for visible and infrared images[J]. IEEE Transactions on Image Processing, 32, 2077-2092(2023).

[139] Luo X, Fu G Z, Yang J X et al. Multi-modal image fusion via deep Laplacian pyramid hybrid network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 7354-7369(2023).

[140] Park S, Vien A G, Lee C. Infrared and visible image fusion using bimodal transformers[C], 1741-1745(2022).

[141] Ma J Y, Tang L F, Fan F et al. SwinFusion: cross-domain long-range learning for general image fusion via swin transformer[J]. IEEE/CAA Journal of Automatica Sinica, 9, 1200-1217(2022).

[142] Li J, Zhu J M, Li C et al. CGTF: convolution-guided transformer for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5012314(2022).

[143] Wang Z S, Chen Y L, Shao W Y et al. SwinFuse: a residual swin transformer fusion network for infrared and visible images[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5016412(2022).

[144] Liu X Z, Gao H J, Miao Q G et al. MFST: multi-modal feature self-adaptive transformer for infrared and visible image fusion[J]. Remote Sensing, 14, 3233(2022).

[145] Vs V, Valanarasu J M J, Oza P et al. Image fusion transformer[C], 3566-3570(2022).

[146] Yi S, Jiang G, Liu X et al. TCPMFNet: an infrared and visible image fusion network with composite auto encoder and transformer-convolutional parallel mixed fusion strategy[J]. Infrared Physics and Technology, 127, 104405(2022).

[147] Kim J H, Hwang Y. Infrared and visible image fusion using a guiding network to leverage perceptual similarity[J]. Computer Vision and Image Understanding, 227, 103598(2023).

[148] Yang X, Huo H T, Wang R H et al. DGLT-Fusion: a decoupled global-local infrared and visible image fusion transformer[J]. Infrared Physics and Technology, 128, 104522(2023).

[149] Chen J, Ding J F, Yu Y et al. THFuse: an infrared and visible image fusion network using transformer and hybrid feature extractor[J]. Neurocomputing, 527, 71-82(2023).

[150] Ji C M, Zhou W J, Lei J S et al. Infrared and visible image fusion via multiscale receptive field amplification fusion network[J]. IEEE Signal Processing Letters, 30, 493-497(2023).

[151] Zhang J, Liu Y, Liu A P et al. Multimodal image fusion via self-supervised transformer[J]. IEEE Sensors Journal, 23, 9796-9807(2023).

[152] Zhang J, Jiao L C, Ma W P et al. Transformer based conditional GAN for multimodal image fusion[J]. IEEE Transactions on Multimedia, 25, 8988-9001(2023).

[153] Rao D Y, Xu T Y, Wu X J. TGFuse: an infrared and visible image fusion approach based on transformer and generative adversarial network[J/OL]. IEEE Transactions on Image Processing, 1. https://ieeexplore.ieee.org/abstract/document/10122870

[154] Roberts J W, van Aardt J A, Ahmed F B. Assessment of image fusion procedures using entropy, image quality, and multispectral classification[J]. Journal of Applied Remote Sensing, 2, 023522(2008).

[155] Guo R[D]. Research on image fusion quality evaluation(2021).

[156] Qu G H, Zhang D L, Yan P F. Information measure for performance of image fusion[J]. Electronics Letters, 38, 313-315(2002).

[157] Haghighat M B A, Aghagolzadeh A, Seyedarabi H. A non-reference image fusion metric based on mutual information of image features[J]. Computers and Electrical Engineering, 37, 744-756(2011).

[158] Hossny M, Nahavandi S, Creighton D. Comments on 'Information measure for performance of image fusion[J]. Electronics Letters, 44, 1066-1067(2008).

[159] Jagalingam P, Hegde A V. A review of quality metrics for fused image[J]. Aquatic Procedia, 4, 133-142(2015).

[160] Zhang X L[D]. Study on some issues of image fusion and performance evaluation(2016).

[161] Rao Y J. In-fibre Bragg grating sensors[J]. Measurement Science and Technology, 8, 355-375(1997).

[162] Eskicioglu A M, Fisher P S. Image quality measures and their performance[J]. IEEE Transactions on Communications, 43, 2959-2965(1995).

[163] Xydeas C S, Petrović V. Objective image fusion performance measure[J]. Electronics Letters, 36, 308-309(2000).

[164] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[165] Li S S, Hong R C, Wu X Q. A novel similarity based quality metric for image fusion[C], 167-172(2008).

[166] Chen Y, Blum R S. A new automated quality assessment algorithm for image fusion[J]. Image and Vision Computing, 27, 1421-1432(2009).

[167] 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).

[168] Liu H X, Zhu T H, Zhao J J. Infrared and visible image fusion based on region of interest detection and nonsubsampled contourlet transform[J]. Journal of Shanghai Jiaotong University (Science), 18, 526-534(2013).

[169] Yue J, Fang L Y, Xia S B et al. Dif-fusion: toward high color fidelity in infrared and visible image fusion with diffusion models[J]. IEEE Transactions on Image Processing, 32, 5705-5720(2023).