深度学习框架下的红外与可见光图像融合方法综述

doi:10.3778/j.issn.1002-8331.2410-0012

摘要/Abstract

摘要： 红外与可见光图像融合（infrared and visible image fusion，IVIF）将红外图像与可见光图像的互补信息融合，提升图像质量以支持下游任务。鉴于深度学习在图像融合方面的优势，将其应用在IVIF领域已成为研究热点。对深度学习框架下的红外与可见光图像融合方法进行梳理分析，根据不同的融合框架将融合方法分为基于自编码器、卷积神经网络、生成对抗网络和变换器，并对比分析这四类方法的特点；综述了IVIF的主要应用领域、常用的6个数据集和8个评价指标，并在典型数据集上对各类主流IVIF方法进行定性和定量评估。最后，总结了现有IVIF方法的局限性，并展望了IVIF的未来研究方向。

关键词: 深度学习, 图像融合, 红外图像, 可见光图像

Abstract: Infrared and visible image fusion （IVIF） fuses complementary information from infrared and visible images to improve image quality and support downstream tasks. Due to the advantages of deep learning in image fusion, its application in IVIF field has become a research hotspot. The infrared and visible image fusion methods deep learning-based are summarized and analyzed. According to different fusion frameworks, the fusion methods are divided into four categories: autoencoder-based, convolutional neural network-based, generative adversarial network-based and Transformer-based. Moreover, the characteristics of each IVIF methods are compared and analyzed. The main application in IVIF fields, 6 common datasets and 8 evaluation metrics are reviewed,and qualitative evaluation and quantitative evaluation of various mainstream IVIF methods on typical datasets are carried out. Finally, the limitations of existing IVIF methods are summarized, and the future IVIF research directions are prospected.

Key words: deep learning, image fusion, infrared image, visible image

李淑慧, 蔡伟, 王鑫, 高蔚洁, 狄星雨. 深度学习框架下的红外与可见光图像融合方法综述[J]. 计算机工程与应用, 2025, 61(9): 25-40.

LI Shuhui, CAI Wei, WANG Xin, GAO Weijie, DI Xingyu. Review of Infrared and Visible Image Fusion Methods in Deep Learning Frameworks[J]. Computer Engineering and Applications, 2025, 61(9): 25-40.

参考文献

[1] SINGH S, SINGH H, BUENO G, et al. A review of image fusion: methods, applications and performance metrics[J]. Digital Signal Processing, 2023, 137: 104020.
[2] TANG Z Y, XU T Y, LI H, et al. Exploring fusion strategies for accurate RGBT visual object tracking[J]. Information Fusion, 2023, 99: 101881.
[3] YIN W X, HE K J, XU D, et al. Adaptive low light visual enhancement and high-significant target detection for infrared and visible image fusion[J]. The Visual Computer, 2023, 39(12): 6723-6742.
[4] LIU J Y, LIU Z, WU G Y, et al. Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 8081-8090.
[5] ZHOU T X, RUAN S, CANU S. A review: deep learning for medical image segmentation using multi-modality fusion[J]. Array, 2019, 3: 100004.
[6] MA J Y, MA Y, LI C. Infrared and visible image fusion methods and applications: a survey[J]. Information Fusion, 2019, 45: 153-178.
[7] LIU Y, WANG L, CHENG J, et al. Multi-focus image fusion: a survey of the state of the art[J]. Information Fusion, 2020, 64: 71-91.
[8] LIANG N N. Medical image fusion with deep neural networks[J]. Scientific Reports, 2024, 14: 7972.
[9] SUN X, TIAN Y, LU W X, et al. From single-to multi-modal remote sensing imagery interpretation: a survey and taxonomy[J]. Science China Information Sciences, 2023, 66(4): 140301.
[10] LI J J, ZHANG J C, YANG C, et al. Comparative analysis of pixel-level fusion algorithms and a new high-resolution dataset for SAR and optical image fusion[J]. Remote Sensing, 2023, 15(23): 5514.
[11] ZHANG K, YAN J, ZHANG F, et al. Spectral-spatial dual graph unfolding network for multispectral and hyperspectral image fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-18.
[12] HAO H T, ZHANG B J, WANG K. MGFuse: an infrared and visible image fusion algorithm based on multiscale decomposition optimization and gradient-weighted local energy[J]. IEEE Access, 2023, 11: 33248-33260.
[13] ZHANG Z Y, HE C Y, WANG H, et al. Fusion of infrared and visible images via multi-layer convolutional sparse representation[J]. Journal of King Saud University - Computer and Information Sciences, 2024, 36(6): 102090.
[14] LIU Y C, DONG L L, XU W H. Infrared and visible image fusion for shipborne electro-optical pod in maritime environment[J]. Infrared Physics & Technology, 2023, 128: 104526.
[15] WANG X, GUAN Z, QIAN W H, et al. Contrast saliency information guided infrared and visible image fusion[J]. IEEE Transactions on Computational Imaging, 2023, 9: 769-780.
[16] ZHANG X C, DEMIRIS Y. Visible and infrared image fusion using deep learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 10535-10554.
[17] 唐霖峰, 张浩, 徐涵, 等. 基于深度学习的图像融合方法综述[J]. 中国图象图形学报, 2023, 28(1): 3-36.
TANG L F, ZHANG H, XU H, et al. Deep learning-based image fusion: a survey[J]. Journal of Image and Graphics, 2023, 28(1): 3-36.
[18] 吴一非, 杨瑞, 吕其深, 等. 红外与可见光图像融合: 统计分析,深度学习方法和未来展望[J]. 激光与光电子学进展, 2024, 61(14): 1400004.
WU Y F, YANG R, LYU Q S, et al. Infrared and visible image fusion: statistical analysis, deep learning approaches and future prospects[J]. Laser & Optoelectronics Progress, 2024, 61(14): 1400004.
[19] 王恩龙, 李嘉伟, 雷佳, 等. 基于深度学习的红外可见光图像融合综述[J]. 计算机科学与探索, 2024, 18(4): 899-915.
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, 2024, 18(4): 899-915.
[20] 张宏钢, 杨海涛, 郑逢杰, 等. 特征级红外与可见光图像融合方法综述[J]. 计算机工程与应用,2024, 60(18): 17-31.
ZHANG H G, YANG H T, ZHENG F J, et al. Review of feature-level infrared and visible image fusion[J]. Computer Engineering and Applications, 2024, 60(18): 17-31.
[21] LUO Y Y, LUO Z Q. Infrared and visible image fusion: methods, datasets, applications, and prospects[J]. Applied Sciences, 2023, 13(19): 10891.
[22] YANG K X, XIANG W, CHEN Z S, et al. A review on infrared and visible image fusion algorithms based on neural networks[J]. Journal of Visual Communication and Image Representation, 2024, 101: 104179.
[23] LI H, WU X J. DenseFuse: a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614-2623.
[24] FU Y, WU X J. A dual-branch network for infrared and visible image fusion[C]//Proceedings of the 2020 25th International Conference on Pattern Recognition. Piscataway: IEEE, 2021: 10675-10680.
[25] 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, 2020, 69(12): 9645-9656.
[26] 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, 2020, 70: 1-15.
[27] XIE Z H, ZONG S, LI Q, et al. Interactive residual coordinate attention and contrastive learning for infrared and visible image fusion in triple frequency bands[J]. Scientific Reports, 2024, 14: 90.
[28] ZHANG G C, NIE R C, CAO J D. SSL-WAEIE: self-supervised learning with weighted auto-encoding and information exchange for infrared and visible image fusion[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(9): 1694-1697.
[29] TANG L F, XIANG X Y, ZHANG H, et al. DIVFusion: darkness-free infrared and visible image fusion[J]. Information Fusion, 2023, 91: 477-493.
[30] ZHANG Z Y, LI H, XU T Y, et al. GuideFuse: a novel guided auto-encoder fusion network for infrared and visible images[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 73: 1-11.
[31] HUANG J X, LI X S, TAN H S, et al. DeDNet: infrared and visible image fusion with noise removal by decomposition-driven network[J]. Measurement, 2024, 237: 115092.
[32] LI J Y, JIANG J J, LIANG P W, et al. MaeFuse: transferring omni features with pretrained masked autoencoders for infrared and visible image fusion via guided training[J]. arXiv:2404.11016, 2024.
[33] LIANG P W, JIANG J J, LIU X M, et al. Fusion from decomposition: a self-supervised decomposition approach for image fusion[C]//Proceedings of the 17th European Conference on Computer Vision. Berlin: Springer, 2022: 719-735.
[34] 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, 2022, 218: 103407.
[35] ZHANG T T, DU H Q, XIE M. W-shaped network: a lightweight network for real-time infrared and visible image fusion[J]. Journal of Electronic Imaging, 2023, 32(6): 063005.
[36] ZHENG B H, XIANG T M, LIN M H, et al. Real-time semantics-driven infrared and visible image fusion network[J]. Sensors, 2023, 23(13): 6113.
[37] ZHANG Y, LIU Y, SUN P, et al. IFCNN: a general image fusion framework based on convolutional neural network[J]. Information Fusion, 2020, 54: 99-118.
[38] ZHU D C, MA J B, LI D, et al. SCGAFusion: a skip-connecting group convolutional attention network for infrared and visible image fusion[J]. Applied Soft Computing, 2024, 163: 111902.
[39] 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, 2023, 30: 493-497.
[40] NIE J Y, SUN H, SUN X, et al. Cross-modal feature fusion and interaction strategy for CNN-transformer-based object detection in visual and infrared remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 21: 1-5.
[41] LI J F, LIU L, SONG H, et al. DCTNet: a heterogeneous dual-branch multi-cascade network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-14.
[42] YANG Y, ZHOU N, WAN W G, et al. MACCNet: multiscale attention and cross- convolutional network for infrared and visible image fusion[J]. IEEE Sensors Journal, 2024, 24(10): 16587-16600.
[43] HU X Y, LIU Y, YANG F. PFCFuse: a poolformer and CNN fusion network for infrared-visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 1-14.
[44] 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, 2022, 32(3): 1186-1196.
[45] 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, 2022, 82: 28-42.
[46] 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, 2018, 16(3): 1850018.
[47] 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(10): 2761-2785.
[48] WU D, WANG Y Z, WANG H R, et al. DCFNet: infrared and visible image fusion network based on discrete wavelet transform and convolutional neural network[J]. Sensors, 2024, 24(13): 4065.
[49] WANG Z T, WANG F, WU D, et al. Infrared and visible image fusion method using salience detection and convolutional neural network[J]. Sensors, 2022, 22(14): 5430.
[50] YANG C X, HE Y N, SUN C, et al. Multi-scale convolutional neural networks and saliency weight maps for infrared and visible image fusion[J]. Journal of Visual Communication and Image Representation, 2024, 98: 104015.
[51] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 2672-2680.
[52] MA J Y, YU W, LIANG P W, et al. FusionGAN: a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 2019, 48: 11-26.
[53] 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, 2020, 70: 1-14.
[54] XU X D, SHEN Y, HAN S. Dense-FG: a fusion GAN model by using densely connected blocks to fuse infrared and visible images[J]. Applied Sciences, 2023, 13(8): 4684.
[55] 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, 2023, 33(2): 549-561.
[56] 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, 2020, 29: 4980-4995.
[57] 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, 2020, 23: 1383-1396.
[58] 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]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 5792-5801.
[59] LU G S, FANG Z L, HE C M, et al. HDDGAN: a heter-ogeneous dual-discriminator generative adversarial network for infrared and visible image fusion[J]. arXiv:2404.15992, 2024.
[60] YIN H T, XIAO J H, CHEN H. CSPA-GAN: a cross-scale pyramid attention GAN for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-11.
[61] 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, 2023, 92: 336-349.
[62] CHANG L, HUANG Y D, LI Q F, et al. DUGAN: infrared and visible image fusion based on dual fusion paths and a U-type discriminator[J]. Neurocomputing, 2024, 578: 127391.
[63] RADFORD A, METZ L, CHINTALA S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv:1511.06434, 2015.
[64] MA J Y, LIANG P W, YU W, et al. Infrared and visible image fusion via detail preserving adversarial learning[J]. Information Fusion, 2020, 54: 85-98.
[65] YUAN C, SUN C Q, TANG X Y, et al. FLGC-fusion GAN: an enhanced fusion GAN model by importing fully learnable group convolution[J]. Mathematical Problems in Engineering, 2020, 2020(1): 6384831.
[66] 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, 2020(1): 3739040.
[67] SONG A Y, DUAN H X, PEI H D, et al. Triple-discriminator generative adversarial network for infrared and visible image fusion[J]. Neurocomputing, 2022, 483: 183-194.
[68] LI K X, LIU G, GU X J, et al. DANT-GAN: a dual attention-based of nested training network for infrared and visible image fusion[J]. Digital Signal Processing, 2024, 145: 104316.
[69] 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, 2021, 25: 635-648.
[70] HOU J L, ZHANG D Z, WU W, et al. A generative adversarial network for infrared and visible image fusion based on semantic segmentation[J]. Entropy, 2021, 23(3): 376.
[71] LI H, GUAN Z, WANG X, et al. Unpaired high-quality image-guided infrared and visible image fusion via generative adversarial network[J]. Computer Aided Geometric Design, 2024, 111: 102325.
[72] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. arXiv:1706.03762, 2017.
[73] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[J]. arXiv:2010.11929, 2020.
[74] VIBASHAN V S, JOSE VALANARASU J M, OZA P, et al. Image fusion transformer[C]//Proceedings of the 2022 IEEE International Conference on Image Processing. Piscataway: IEEE, 2022: 3566-3570.
[75] 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, 2022, 71: 1-12.
[76] LI J, YANG B, BAI L, et al. TFIV: multigrained token fusion for infrared and visible image via transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-14.
[77] LI H, WU X J. CrossFuse: a novel cross attention mechanism based infrared and visible image fusion approach[J]. Information Fusion, 2024, 103: 102147.
[78] TANG W, HE F Z, LIU Y. YDTR: infrared and visible image fusion via Y-shape dynamic transformer[J]. IEEE Transactions on Multimedia, 2022, 25: 5413-5428.
[79] ZHAO Z X, BAI H W, ZHANG J S, et al. CDDFuse: correlation-driven dual-branch feature decomposition for multi-modality image fusion[J]. arXiv:2211.14461, 2022.
[80] 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]. arXiv:2201.10147, 2022.
[81] JIANG C C, REN H Z, YANG H, et al. M2FNet: multi-modal fusion network for object detection from visible and thermal infrared images[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 130: 103918.
[82] 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, 2023, 33(7): 3159-3172.
[83] TANG W, HE F Z, LIU Y. TCCFusion: an infrared and visible image fusion method based on transformer and cross correlation[J]. Pattern Recognition, 2023, 137: 109295.
[84] YAO S C, PIAO A L, JIANG W J, et al. STFNets: learning sensing signals from the time-frequency perspective with short-time Fourier neural networks[C]//Proceedings of the World Wide Web Conference. New York: ACM, 2019: 2192-2202.
[85] LIU Y, LI X Y, LIU Y, et al. SimpliFusion: a simplified infrared and visible image fusion network[J]. The Visual Computer, 2025, 41(2): 1335-1350.
[86] LUO Y Y, LUO Z Q. Infrared and visible image fusion algorithm based on improved residual swin transformer and sobel operators[J]. IEEE Access, 2024, 12: 82134-82145.
[87] 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, 2023, 527: 71-82.
[88] XIN D, XU L X, CHEN H M, et al. A vehicle target detection method based on feature level fusion of infrared and visible light image[C]//Proceedings of the 2022 34th Chinese Control and Decision Conference. Piscataway: IEEE, 2022: 469-474.
[89] KANG X D, YIN H, DUAN P H. Global-local feature fusion network for visible-infrared vehicle detection[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1-5.
[90] MARQUES T, CARREIRA S, MIRAGAIA R, et al. Applying deep learning to real-time UAV-based forest monitoring: leveraging multi-sensor imagery for improved results[J]. Expert Systems with Applications, 2024, 245: 123107.
[91] LIU F Y, LIU J, WANG L B, et al. Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning[J]. Automation in Construction, 2024, 161: 105355.
[92] WANG P J, XIAO J Z, QIANG X X, et al. An automatic building fa?ade deterioration detection system using infrared-visible image fusion and deep learning[J]. Journal of Building Engineering, 2024, 95: 110122.
[93] WEN H, HU X K, ZHONG P. Detecting rice straw burning based on infrared and visible information fusion with UAV remote sensing[J]. Computers and Electronics in Agriculture, 2024, 222: 109078.
[94] ZHAO X R, LIU Y Y, HUANG Z B, et al. Early diagnosis of cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion[J]. Scientific Reports, 2024, 14: 20176.
[95] GUO S, WU K, JEON S, et al. IETAFusion: an illumination enhancement and target-aware infrared and visible image fusion network for security system of smart city[J]. Expert Systems, 2025, 42(1): e13538.
[96] WANG L, ZHAO P H, CHU N, et al. A hierarchical Bayesian fusion method of infrared and visible images for temperature monitoring of high-speed direct-drive blower[J]. IEEE Sensors Journal, 2022, 22(19): 18815-18830.
[97] HE Y Z, WANG Y X, WU F W, et al. Temperature monitoring of vehicle brake drum based on dual light fusion and deep learning[J]. Infrared Physics & Technology, 2023, 133: 104823.
[98] CIPRIáN-SáNCHEZ J F, OCHOA-RUIZ G, GONZALEZ-MENDOZA M, et al. FIRe-GAN: a novel deep learning-based infrared-visible fusion method for wildfire imagery[J]. Neural Computing and Applications, 2023, 35(25): 18201-18213.
[99] SHANG D F, ZHANG F, YUAN D P, et al. Deep learning-based forest fire risk research on monitoring and early warning algorithms[J]. Fire, 2024, 7(4): 151.
[100 BARTUZI E, TROKIELEWICZ M. Multispectral hand features for secure biometric authentication systems[J]. Concurrency and Computation: Practice and Experience, 2021, 33(18): e6471.
[101] SU L, FEI L K, ZHAO S P, et al. Learning modality-invariant binary descriptor for crossing palmprint to palm-vein recognition[J]. Pattern Recognition Letters, 2023, 172: 1-7.
[102] FEI L K, SU L, ZHANG B, et al. Learning frequency-aware common feature for VIS-NIR heterogeneous palmprint recognition[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 7604-7618.
[103] YANG Y M, HU W P, HU H F. Neutral face learning and progressive fusion synthesis network for NIR-VIS face recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(10): 5750-5763.
[104] ABDUL-AL M, KYEREMEH G K, QAHWAJI R, et al. A novel approach to enhancing multi-modal facial recognition: integrating convolutional neural networks, principal component analysis, and sequential neural networks[J]. IEEE Access, 2024, 12: 140823-140846.
[105] NASEEM M T, LEE C S, KIM N H. Facial expression recognition using visible, IR, and MSX images by early and late fusion of deep learning models[J]. IEEE Access, 2024, 12: 20692-20704.
[106] TOET A. The TNO multiband image data collection[J]. Data in Brief, 2017, 15: 249-251.
[107] 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, 2022, 44(1): 502-518.
[108] JIA X Y, ZHU C, LI M Z, et al. LLVIP: a visible-infrared paired dataset for low-light vision[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops. Piscataway: IEEE, 2021: 3489-3497.
[109] OCHS M, KRETZ A, MESTER R. SDNet: semantically guided depth estimation network[J]. arXiv:1907.10659, 2019.
[110] LI H, WU X J, KITTLER J. RFN-Nest: an end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 2021, 73: 72-86.
[111] 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, 2022, 83: 79-92.
[112] SHEN J F, CHEN Y F, LIU Y, et al. ICAFusion: iterative cross-attention guided feature fusion for multispectral object detection[J]. Pattern Recognition, 2024, 145: 109913.