Deep learning-based image fusion: a survey

Linfeng Tang; Hao Zhang; Han Xu; Jiayi Ma

doi:10.11834/jig.220422

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Deep learning-based image fusion: a survey
Vol. 28, Issue 1, Pages: 3-36(2023)
Received：18 May 2022，

Revised：2022-7-18，

Accepted：25 July 2022，

Published：16 January 2023
DOI： 10.11834/jig.220422
稿件说明：

移动端阅览

DOI：

Linfeng Tang, Hao Zhang, Han Xu, Jiayi Ma. Deep learning-based image fusion: a survey[J]. Journal of Image and Graphics, 2023, 28(1): 3-36. DOI： 10.11834/jig.220422.

摘要

图像融合技术旨在将不同源图像中的互补信息整合到单幅融合图像中以全面表征成像场景，并促进后续的视觉任务。随着深度学习的兴起，基于深度学习的图像融合算法如雨后春笋般涌现，特别是自编码器、生成对抗网络以及Transformer等技术的出现使图像融合性能产生了质的飞跃。本文对不同融合任务场景下的前沿深度融合算法进行全面论述和分析。首先，介绍图像融合的基本概念以及不同融合场景的定义。针对多模图像融合、数字摄影图像融合以及遥感影像融合等不同的融合场景，从网络架构和监督范式等角度全面阐述各类方法的基本思想，并讨论各类方法的特点。其次，总结各类算法的局限性，并给出进一步的改进方向。再次，简要介绍不同融合场景中常用的数据集，并给出各种评估指标的具体定义。对于每一种融合任务，从定性评估、定量评估和运行效率等多角度全面比较其中代表性算法的性能。本文提及的算法、数据集和评估指标已汇总至

https://github.com/Linfeng-Tang/Image-Fusion

。最后，给出了本文结论以及图像融合研究中存在的一些严峻挑战，并对未来可能的研究方向进行了展望。

Abstract

Image fusion aims to integrate complementary information from multi-source images into a single fused image to characterize the imaging scene and facilitate the subsequent vision tasks further. In recent years

it has been a concern in the field of image processing

especially in artificial intelligence-related industries like intelligent medical service

autonomous dri

ving

smart photography

security surveillance

and military monitoring. Moreover

the growth of deep learning has been promoting deep learning-based image fusion algorithms. In particular

the emergence of advanced techniques

such as the auto-encoder

generative adversarial network

and Transformer

has led to a qualitative leap in image fusion performance. However

a comprehensive review and analysis of state-of-the-art deep learning-based image fusion algorithms for different fusion scenarios are required to be realized. Thus

we develop a systematic and critical review to explore the developments of image fusion in recent years. First

a comprehensive and systematic introduction of the image fusion field is presented from the following three aspects: 1) the development of image fusion technology

2) the prevailing datasets

and 3) the common evaluation metrics. Then

more extensive qualitative experiments

quantitative experiments

and running efficiency evaluations of representative image fusion methods are conducted on the public datasets to compare their performance. Finally

the summary and challenges in the image fusion community are highlighted. In particular

some prospects are recommended further in the field of image fusion. First of all

from the perspective of fusion scenarios

the existing image fusion methods can be divided into three categories

i.e.

multi-modal image fusion

digital photography image fusion

and remote sensing image fusion. Specifically

multi-modal image fusion is composed of infrared and visible image fusion as well as medical image fusion

and digital photography image fusion consists of multi-exposure image fusion as well as multi-focus image fusion. Multi-spectral and panchromatic image fusion can be as one of the key aspects for remote sensing image fusion. In addition

the domain of deep learning-based image fusion algorithms can be classified into the auto-encoder based (AE-based) fusion framework

convolutional neural network based (CNN-based) fusion framework

and g

enerative adversarial network based (GAN-based) fusion framework from the aspect of network architectures. The AE-based fusion framework achieves the feature extraction and image reconstruction by a pre-trained auto-encoder

and accomplishes deep feature fusion via manual fusion strategies. To clarify feature extraction

fusion

and image reconstruction

the CNN-based fusion framework is originated from detailed network structures and loss functions. The GAN-based framework defines the image fusion problem as an adversarial game between the generators and discriminators. From the perspective of the supervision paradigm

the deep learning fusion methods can also be categorized into three classes

i.e.

unsupervised

self-supervised

and supervised. The supervised methods leverage ground truth value to guide the training processes

and the unsupervised approaches construct loss function via constraining the similarity between the fusion result and source images. The self-supervised algorithms are associated with the AE-based framework in common. Our critical review is focused on the main concepts and discussions of the characteristics of each method for different fusion scenarios from the perspectives of the network architecture and supervision paradigm. Especially

we summarize the limitations of different fusion algorithms and provide some recommendations for further research. Secondly

we briefly introduce the popular public datasets and provide the interfaces-related to download them for each specific image fusion scenario. Then

we present the common evaluation metrics in the image fusion field from two aspects: regular-based evaluation metrics and specific-based metrics designed for pan-sharpening. The generic metrics can be utilized to evaluate multi-modal and digital photography image fusion algorithms of those are entropy-based

correlation-based

image feature-based

image structure-based

and human visual perception-based metrics in total. Some of the generic metrics

such as peak signal-to-noise ratio (

PSNR)

correlation coefficient (CC)

structural similarity index measure (SSIM)

and visual information fidelity (VIF)

are also used for the quantitative assessment of pan-sharpening. The specific metrics designed for pan-sharpening consist of no-reference metrics and full-reference metrics that employ the full-resolution image as the reference image

i.e.

ground truth. Thirdly

we present the qualitative/quantitative results

and average running times of representative alternatives for various fusion missions. Finally

this review has critically analyzed the conclusion

highlights the challenges in the image fusion community

and carried out forecasting analysis

such as non-registered image fusion

high-level vision task-driven image fusion

cross-resolution image fusion

real-time image fusion

color image fusion

image fusion based on physical imaging principles

image fusion under extreme conditions

and comprehensive evaluation metrics

etc. The methods

datasets

and evaluation metrics mentioned are linked at:

https://github.com/Linfeng-Tang/Image-Fusion

关键词

Keywords

references

Adu J, Gan J H, Wang Y and Huang J. 2013. Image fusion based on nonsubsampled contourlet transform for infrared and visible light image. Infrared Physics and Technology, 61: 94-100 [DOI: 10.1016/j.infrared.2013.07.010]

Alparone L, Aiazzi B, Baronti S, Garzelli A, Nencini F and Selva M. 2008. Multispectral and panchromatic data fusion assessment without reference. Photogrammetric Engineering and Remote Sensing, 74(2): 193-200 [DOI: 10.14358/PERS.74.2.193]

Alparone L, Baronti S, Garzelli A and Nencini F. 2004. A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1(4): 313-317 [DOI: 10.1109/LGRS.2004.836784]

Amin-Naji M, Aghagolzadeh A and Ezoji M. 2019. Ensemble of CNN for multi-focus image fusion. Information Fusion, 51: 201-214 [DOI: 10.1016/j.inffus.2019.02.003]

Aslantas V and Bendes E. 2015. A new image quality metric for image fusion: the sum of the correlations of differences. AEU-International Journal of Electronics and Communications, 69(12): 1890-1896 [DOI: 10.1016/j.aeue.2015.09.004]

Benzenati T, Kessentini Y and Kallel A. 2022. Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks. Expert Systems with Applications, 188: #115996 [DOI: 10.1016/j.eswa.2021.115996]

Bhatnagar G, Wu Q M J and Liu Z. 2013. Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Transactions on Multimedia, 15(5): 1014-1024 [DOI: 10.1109/TMM.2013.2244870]

Cai J J and Huang B. 2021. Super-resolution-guided progressive pansharpening based on a deep convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59(6): 5206-5220 [DOI: 10.1109/TGRS.2020.3015878]

Cai J R, Gu S H andZhang L. 2018. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4): 2049-2062 [DOI: 10.1109/TIP.2018.2794218]

Cao Y P, Guan D Y, Huang W L, Yang J X, Cao Y L and Qiao Y. 2019. Pedestrian detection with unsupervised multispectral feature learning using deep neural networks. Information Fusion, 46: 206-217 [DOI: 10.1016/j.inffus.2018.06.005]

Chen G Y, Wu X J and Xu T Y. 2022. Unsupervised infrared image and visible image fusion algorithm based on deep learning. Laser and Optoelectronics Progress, 59(4): #0410010

陈国洋, 吴小俊, 徐天阳. 2022. 基于深度学习的无监督红外图像与可见光图像融合算法. 激光与光电子学进展, 59(4): #0410010 [DOI: 10.3788/LOP202259.0410010]

Chen J, Li X J, Luo L B, Mei X G and Ma J Y. 2020. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Information Sciences, 508: 64-78 [DOI: 10.1016/j.ins.2019.08.066]

Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the 15th European Conference on Computer Vsion. Munich, Germany: Springer: 833-851 [ DOI: 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49 ]

Chen Y and Blum R S. 2009. A new automated quality assessment algorithm for image fusion. Image and Vision Computing,27(10): 1421-1432 [DOI: 10.1016/j.imavis.2007.12.002]

Cui G M, Feng H J, Xu Z H, Li Q and Chen Y T. 2015. Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optics Communications, 341: 199-209 [DOI: 10.1016/j.optcom.2014.12.032]

Cvejic N, Bull D and Canagarajah N. 2007. Region-based multimodal image fusion using ICA bases. IEEE Sensors Journal, 7(5): 743-751 [DOI: 10.1109/JSEN.2007.894926]

Deng J, Dong W, Socher R, Li L J, Li K and Li F F. 2009. ImageNet: a large-scale hierarchical image database//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE: 248-255 [ DOI: 10.1109/CVPR.2009.5206848 http://dx.doi.org/10.1109/CVPR.2009.5206848 ]

Deng X and Dragotti P L. 2021. Deep convolutional neural network for multi-modal image restoration and fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10): 3333-3348 [DOI: 10.1109/TPAMI.2020.2984244]

Deng X, Zhang Y T, Xu M, Gu S H and Duan Y P. 2021. Deep coupled feedback network for joint exposure fusion and image super-resolution. IEEE Transactions on Image Processing, 30: 3098-3112 [DOI: 10.1109/TIP.2021.3058764]

Dong M L, Li W S, Liang X S and Zhang X Y. 2021. MDCNN: multispectral pansharpening based on a multiscale dilated convolutional neural network. Journal of Applied Remote Sensing, 15(3): #036516 [DOI: oi.org/10.1117/1.JRS.15.036516]

Eskicioglu A M and Fisher P S. 1995. Image quality measures and their performance. IEEE Transactions on Communications, 43(12): 2959-2965 [DOI: 10.1109/26.477498]

Fu J, Li WS, Du J and Huang Y P. 2021a. A multiscale residual pyramid attention network for medical image fusion. Biomedical Signal Processing and Control, 66: #102488 [DOI: 10.1016/j.bspc.2021.102488]

Fu Y, Wu X J and Durrani T. 2021b. Image fusion based on generative adversarial network consistent with perception. Information Fusion, 72: 110-125 [DOI: 10.1016/j.inffus.2021.02.019]

Fu Z Z, Wang X, Xu J, Zhou N and Zhao Y F. 2016. Infrared and visible images fusion based on RPCA and NSCT. Infrared Physics and Technology, 77: 114-123 [DOI: 10.1016/j.infrared.2016.05.012]

Garzelli A and Nencini F. 2009. Hypercomplex quality assessment of multi/hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 6(4): 662-665 [DOI: 10.1109/LGRS.2009.2022650]

Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press: 2672-2680 [ DOI: 10.5555/2969033.2969125 http://dx.doi.org/10.5555/2969033.2969125 ]

Guo A J, Dian R W and Li S T. 2020. Unsupervised blur kernel learning for pansharpenin g//2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa, USA: IEEE: 633-636 [ DOI: 10.1109/IGARSS39084.2020.9324543 http://dx.doi.org/10.1109/IGARSS39084.2020.9324543 ]

Guo X P, Nie R C, Cao J D, Zhou D M, Mei L Y and He K J. 2019. FuseGAN: learning to fuse multi-focus image via conditional generative adversarial network. IEEE Transactions on Multimedia, 21(8): 1982-1996 [DOI: 10.1109/TMM.2019.2895292]

Ha Q S, Watanabe K, Karasawa T, Ushiku Y and Harada T. 2017. MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE: 5108-5115 [ DOI: 10.1109/IROS.2017.8206396 http://dx.doi.org/10.1109/IROS.2017.8206396 ]

Haghighat M B A, Aghagolzadeh A and Seyedarabi H. 2011. A non-reference image fusion metric based on mutual information of image features. Computers and Electrical Engineering, 37(5): 744-756 [DOI: 10.1016/j.compeleceng.2011.07.012]

Han D, Li L, Guo X J and Ma J Y. 2022. Multi-exposure image fusion via deep perceptual enhancement. Information Fusion, 79: 248-262 [DOI: 10.1016/j.inffus.2021.10.006]

Han Y, Cai Y Z, Cao Y and Xu X M. 2013. A new image fusion performance metric based on visual information fidelity. Information Fusion, 14(2): 127-135 [DOI: 10.1016/j.inffus.2011.08.002]

He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 770-778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]

Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 2261-2269 [ DOI: 10.1109/CVPR.2017.243 http://dx.doi.org/10.1109/CVPR.2017.243 ]

Huang J, Le Z L, Ma Y, Fan F, Zhang H and Yang L. 2020a. MGMDcGAN: medical image fusion using multi-generator multi-discriminator conditional generative adversarial network. IEEE Access, 8: 55145-55157 [DOI: 10.1109/ACCESS.2020.2982016]

Huang J, Le Z L, Ma Y, Mei X G and Fan F. 2020b. A generative adversarial network with adaptive constraints for multi-focus image fusion. Neural Computing and Applications, 32(18): 15119-15129 [DOI: 10.1007/s00521-020-04863-1]

Huo X, Zou Y, Chen Y and Tan J Q. 2021. Dual-scale decomposition and saliency analysis based infrared and visible image fusion. Journal of Image and Graphics, 26(12): 2813-2825

霍星, 邹韵, 陈影, 檀结庆. 2021. 双尺度分解和显著性分析相结合的红外与可见光图像融合. 中国图象图形学报, 26(12): 2813-2825 [DOI: 10.11834/jig.200405]

Jagalingam P and Hegde A V. 2015. A review of quality metrics for fused image. Aquatic Procedia, 4: 133-142 [DOI: 10.1016/j.aqpro.2015.02.019]

Jia X Y, Zhu C Z, Li M, Tang W Q and Zhou W L, 2021. LLVIP: A Visible-infrared Paired Dataset for Low-light Vision//Proceedings of 2021 IEEE/CVF International Conferenceon Computer Vision Workshops. Montreal, Canada: IEEE: 3489-3497

Jian L H, Yang X M, Liu Z, Jeon G, Gao M L and Chisholm D. 2021. SEDRFuse: a symmetric encoder-decoder with residual block network for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70: 1-15 [DOI: 10.1109/TIM.2020.3022438]

Jiang X Y, Ma J Y, Xiao G B, Shao Z F and Guo X J. 2021. A review of multimodal image matching: methods and applications. Information Fusion, 73: 22-71 [DOI: 10.1016/j.inffus.2021.02.012]

Jiao J and Wu L D. 2019. Fusion of multispectral and panchromatic images via morphological filter and improved PCNN in NSST domain. Journal of Image and Graphics, 24(3): 435-446

焦姣, 吴玲达. 2019. 形态学滤波和改进PCNN的NSST域多光谱与全色图像融合. 中国图象图形学报, 24(3): 435-446 [DOI: 10.11834/jig.180399]

Jung H, Kim Y, Jang H, Ha N and Sohn K. 2020. Unsupervised deep image fusion with structure tensor representations. IEEE Transactions on Image Processing, 29: 3845-3858 [DOI: 10.1109/TIP.2020.2966075]

Lahoud F and Süsstrunk S. 2019. Zero-learning fast medical image fusion//Proceedings of the 22nd International Conference on Information Fusion. Ottawa, Canada: IEEE: 1-8 [ DOI: 10.23919/FUSION43075.2019.9011178 http://dx.doi.org/10.23919/FUSION43075.2019.9011178 ]

Lee J, Seo S and Kim M. 2021. SIPSA-Net: shift-invariant pan sharpening with moving object alignment for satellite imagery//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 10161-10169 [ DOI: 10.1109/CVPR46437.2021.01003 http://dx.doi.org/10.1109/CVPR46437.2021.01003 ]

Li H and Wu X J. 2019. DenseFuse: a fusion approach to infrared and visible images. IEEE Transactions on Image Processing, 28(5): 2614-2623 [DOI: 10.1109/TIP.2018.2887342]

Li H and Zhang L. 2018. Multi-exposure fusion with CNN features//Proceedings of the 25th International Conference on Image Processing. Athens, Greece: IEEE: 1723-1727 [ DOI: 10.1109/ICIP.2018.8451689 http://dx.doi.org/10.1109/ICIP.2018.8451689 ]

Li H, Wu X J and Durrani T. 2020a. NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial/channel attention models. IEEE Transactions on Instrumentation and Measurement, 69(12): 9645-9656 [DOI: 10.1109/TIM.2020.3005230]

Li H, Wu X J and Kittler J. 2020b. MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Transactions on Image Processing, 29: 4733-4746 [DOI: 10.1109/TIP.2020.2975984]

Li H, Wu X J and Kittler J. 2021a. RFN-Nest: an end-to-end residual fusion network for infrared and visible images. Information Fusion, 73: 72-86 [DOI: 10.1016/j.inffus.2021.02.023]

Li H F, Cen Y L, Liu Y, Chen X and Yu Z T. 2021b. Different input resolutions and arbitrary output resolution: a meta learning-based deep framework for infrared and visible image fusion. IEEE Transactions on Image Processing, 30: 4070-4083 [DOI: 10.1109/TIP.2021.3069339]

Li J, Huo H T, Li C, Wang R H and Feng Q. 2021c. AttentionFGAN: infrared and visible image fusion using attention-based generative adversarial networks. IEEE Transactions on Multimedia, 23: 1383-1396 [DOI: 10.1109/TMM.2020.2997127]

Li J, Huo H T, Li C, Wang R H, Sui C H and Liu Z. 2021d. Multigrained attention network for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70: #5002412 [DOI: 10.1109/TIM.2020.3029360]

Li J X, Guo X B, Lu G M, Zhang B, Xu Y, Wu F and Zhang D. 2020c. DRPL: deep regression pair learning for multi-focus image fusion. IEEE Transactions on Image Processing, 29: 4816-4831 [DOI: 10.1109/TIP.2020.2976190]

Li S T, Kang X D, Fang L Y, Hu J W and Yin H T. 2017. Pixel-level image fusion: a survey of the state of the art. Information Fusion, 33: 100-112 [DOI: 10.1016/j.inffus.2016.05.004]

Li Y and Wu X J. 2014. Image fusion based on sparse representation using Shannon entropy weighting. ACTA Automatica Sinica, 40(8): 1819-1835

李奕, 吴小俊. 2014. 香农熵加权稀疏表示图像融合方法研究. 自动化学报, 40(8): 1819-1835 [DOI: 10.3724/SP.J.1004.2014.01819]

Li Z X, Liu J Y, Liu R S, Fan X, Luo Z X and Gao W. 2021e. Multiple task-oriented encoders for unified image fusion//Proceedings of 2021 IEEE International Conference on Multimedia and Expo. Shenzhen, China: IEEE: 1-6 [ DOI: 10.1109/ICME51207.2021.9428212 http://dx.doi.org/10.1109/ICME51207.2021.9428212 ]

Liang X C, Hu P Y, Zhang L G, Sun J G and Yin G S. 2019. MCFNet: multi-layer concatenation fusion network for medical images fusion. IEEE Sensors Journal, 19(16): 7107-7119 [DOI: 10.1109/jsen.2019.2913281]

Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P and Zitnick C L. 2014. Microsoft COCO: common objects in context//Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer: 740-755 [ DOI: 10.1007/978-3-319-10602-1_48 http://dx.doi.org/10.1007/978-3-319-10602-1_48 ]

Liu J Y, Fan X, Huang Z B, Wu G Y, Liu R S, Zhong W and Luo Z X. 2022a. Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans, USA: IEEE: 5802-5811

Liu J Y, Fan X, Jiang J, Liu R S and Luo Z X. 2022b. Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion. IEEE Transactions on Circuits and Systems for Video Technology, 32(1): 105-119 [DOI: 10.1109/TCSVT.2021.3056725]

Liu J Y, Shang J J, Liu R S and Fan X. 2022c. Attention-guided global-local adversarial learning for detail-preserving multi-exposure image fusion. IEEE Transactions on Circuits and Systems for Video Technology, 32(8): 5026-5040 [DOI: 10.1109/TCSVT.2022.3144455]

Liu Q J, Zhou H Y, Xu Q Z, Liu X Y and Wang Y H. 2021a. PSGAN: a generative adversarial network for remote sensing image pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 59(12): 10227-10242 [DOI: 10.1109/TGRS.2020.3042974]

Liu R S, Liu J Y, Jiang Z Y, Fan X and Luo Z X. 2021b. A bilevel integrated model with data-driven layer ensemble for multi-modality image fusion. IEEE Transactions on Image Processing, 30: 1261-1274 [DOI: 10.1109/TIP.2020.3043125]

Liu R S, Liu Z, Liu J Y and Fan X. 2021c. Searching a hierarchically aggregated fusion architecture for fast multi-modality image fusion//Proceedings of the 29th ACM International Conference on Multimedia. Chengdu, China: Association for Computing Machinery: 1600-1608 [ DOI: 10.1145/3474085.3475299 http://dx.doi.org/10.1145/3474085.3475299 ]

Liu X B, Mei W B and Du H Q. 2017a. Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing, 235: 131-139 [DOI: 10.1016/j.neucom.2017.01.006]

Liu X Y, Liu Q J and Wang Y H. 2020. Remote sensing image fusion based on two-stream fusion network. Information Fusion, 55: 1-15 [DOI: 10.1016/j.inffus.2019.07.010]

Liu Y, Chen X, Cheng J and Peng H. 2017b. A medical image fusion method based on convolutional neural networks//Proceedings of the 20th International Conference on Information Fusion. Xi'an, China: IEEE: 1-7 [ DOI: 10.23919/ICIF.2017.8009769 http://dx.doi.org/10.23919/ICIF.2017.8009769 ]

Liu Y, Chen X, Peng H and Wang Z F. 2017c. Multi-focus image fusion with a deep convolutional neural network. Information Fusion, 36: 191-207 [DOI: 10.1016/j.inffus.2016.12.001]

Liu Y, Chen X, Wang Z F, Wang Z J, Ward R K and Wang X S. 2018. Deep learning for pixel-level image fusion: recent advances and future prospects. Information Fusion, 42: 158-173 [DOI: 10.1016/j.inffus.2017.10.007]

Liu Y, Chen X, Ward R K and Wang Z J. 2016. Image fusion with convolutional sparse representation. IEEE Signal Processing Letters, 23(12): 1882-1886 [DOI: 10.1109/LSP.2016.2618776]

Liu Y P, Jin J, Wang Q, Shen Y and Dong X Q. 2014. Region level based multi-focus image fusion using quaternion wavelet and normalized cut. Signal Processing, 97: 9-30 [DOI: 10.1016/j.sigpro.2013.10.010]

Long Y Z, Jia H T, Zhong Y D, Jiang Y D and Jia Y M. 2021. RXDNFuse: a aggregated residual dense network for infrared and visible image fusion. Information Fusion, 69: 128-141 [DOI: 10.1016/j.inffus.2020.11.009]

Lou J Q, Li J F and Dai W Z. 2017. Medical image fusion using non-subsampled shearlet transform. Journal of Image and Graphics, 22(11): 1574-1583

楼建强, 李俊峰, 戴文战. 2017. 非下采样剪切波变换的医学图像融合. 中国图象图形学报, 22(11): 1574-1583 [DOI: 10.11834/jig.170014]

Luo S Y, Zhou S B, Feng Y and Xie J G. 2020. Pansharpening via unsupervised convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 4295-4310 [DOI: 10.1109/JSTARS.2020.3008047]

Luo X Q, Gao Y H, Wang A Q, Zhang Z C and Wu X J. 2021. IFSepR: a general framework for image fusion based on separate representation learning. IEEE Transactions on Multimedia [DOI: 10.1109/TMM.2021.3129354]

Ma B Y, Zhu Y, Yin X, Ban X J, Huang H Y and Mukeshimana M. 2021a. SESF-Fuse: an unsupervised deep model for multi-focus image fusion. Neural Computing and Applications, 33(11): 5793-5804 [DOI: 10.1007/s00521-020-05358-9]

Ma H Y, Liao Q M, Zhang J C, Liu S J and Xue J H. 2020a. An α-matte boundary defocus model-based cascaded network for multi-focus image fusion. IEEE Transactions on Image Processing, 29: 8668-8679 [DOI: 10.1109/TIP.2020.3018261]

Ma J L, Zhou Z Q, Wang B and Zong H. 2017. Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrared Physics and Technology, 82: 8-17 [DOI: 10.1016/j.infrared.2017.02.005]

Ma J Y, Chen C, Li C and Huang J. 2016. Infrared and visible image fusion via gradient transfer and total variation minimization. Information Fusion, 31: 100-109 [DOI: 10.1016/j.inffus.2016.02.001]

Ma J Y, Le Z L, Tian X and Jiang J J. 2021b. SMFuse: multi-focus image fusion via self-supervised mask-optimization. IEEE Transactions on Computational Imaging, 7: 309-320 [DOI: 10.1109/TCI.2021.3063872]

Ma J Y, Liang P W, Yu W, Chen C, Guo X J, Wu J and Jiang J J. 2020b. Infrared and visible image fusion via detail preserving adversarial learning. Information Fusion, 54: 85-98 [DOI: 10.1016/j.inffus.2019.07.005]

Ma J Y, Ma Y and Li C. 2019a. Infrared and visible image fusion methods and applications: a survey. Information Fusion, 45: 153-178 [DOI: 10.1016/j.inffus.2018.02.004]

Ma J Y, Tang L F, Fan F, Huang J, Mei X G and Ma Y. 2022. SwinFusion: cross-domain long-range learning for general image fusion via Swin Transformer. IEEE/CAA Journal of Automatica Sinica, 9(7): 1200-1217 [DOI: 10.1109/JAS.2022.105686]

Ma J Y, Tang L F, Xu M L, Zhang H and Xiao G B. 2021c. STDFusionNet: an infrared and visible image fusion network based on salient target detection. IEEE Transactions on Instrumentation and Measurement, 70: 1-13 [DOI: 10.1109/TIM.2021.3075747]

Ma J Y, Xu H, Jiang J J, Mei X G and Zhang X P. 2020c. DDcGAN: adual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Transactions on Image Processing, 29: 4980-4995 [DOI: 10.1109/TIP.2020.2977573]

Ma J Y, Yu W, Chen C, Liang P W, Guo X J and Jiang J J. 2020d. Pan-GAN: an unsupervised pan-sharpening method for remote sensing image fusion. Information Fusion, 62: 110-120 [DOI: 10.1016/j.inffus.2020.04.006]

Ma J Y, Yu W, Liang P W, Li C and Jiang J J. 2019b. FusionGAN: a generative adversarial network for infrared and visible image fusion. Information Fusion, 48: 11-26 [DOI: 10.1016/j.inffus.2018.09.004]

Ma J Y, Zhang H, Shao Z F, Liang P W and Xu H. 2021d. GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70: #5005014 [DOI: 10.1109/TIM.2020.3038013]

Ma K D, Duanmu Z F, Zhu H W, Fang Y M and Wang Z. 2020e. Deep guided learning for fast multi-exposure image fusion. IEEE Transactions on Image Processing, 29: 2808-2819 [DOI: 10.1109/TIP.2019.2952716]

Ma N, Zhou Z M, Zhang P and Luo L M. 2013. A new variational model for panchromatic and multispectral image fusion. Acta Automatica Sinica, 39(2): 179-187

马宁, 周则明, 张鹏, 罗立民. 2013. 一种新的全色与多光谱图像融合变分模型. 自动化学报, 39(2): 179-187 [DOI: 10.3724/SP.J.1004.2013.00179]

Masi G, Cozzolino D, Verdoliva L and Scarpa G. 2016. Pansharpening by convolutional neural networks. Remote Sensing, 8(7): #594 [DOI: 10.3390/rs8070594]

Mou J, Gao W and Song Z X. 2013. Image fusion based on non-negative matrix factorization and infrared feature extraction//Proceedings of the 6th International Congress on Image and Signal Processing. Hangzhou, China: IEEE: 1046-1050 [ DOI: 10.1109/CISP.2013.6745210 http://dx.doi.org/10.1109/CISP.2013.6745210 ]

Nejati M, Samavi S and Shirani S. 2015. Multi-focus image fusion using dictionary-based sparse representation. Information Fusion, 25: 72-84 [DOI: 10.1016/j.inffus.2014.10.004]

Ni J H, Shao Z M, Zhang Z Z, Hou M Z, Zhou J L, Fang L Y and Zhang Y. 2021. LDP-Net: an unsupervised pansharpening network based on learnable degradation processes [EB/OL ] . [2021-11-24 ] . https://arxiv.org/pdf/2111.12483.pdf https://arxiv.org/pdf/2111.12483.pdf

Pan Z Y, Yu M, Jiang G Y, Xu H Y, Peng Z J and Chen F. 2020. Multi-exposure high dynamic range imaging with informative content enhanced network. Neurocomputing, 386: 147-164 [DOI: 10.1016/j.neucom.2019.12.093]

Petrovic V and Xydeas C. 2005. Objective image fusion performance characterisation//Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE: 1866-1871 [ DOI: 10.1109/ICCV.2005.175 http://dx.doi.org/10.1109/ICCV.2005.175 ]

Prabhakar K R, Srikar V S and Babu R V. 2017. DeepFuse: a deep unsupervised approach for exposure fusion with extreme exposure image pairs//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 4724-4732 [ DOI: 10.1109/ICCV.2017.505 http://dx.doi.org/10.1109/ICCV.2017.505 ]

Qi Y, Zhou S B, Zhang Z H, Luo S Y, Lin X R, Wang L P and Qiang B H. 2021. Deep unsupervised learning based on color un-referenced loss functions for multi-exposure image fusion. Information Fusion, 66: 18-39 [DOI: 10.1016/j.inffus.2020.08.012]

Qu G H, Zhang D L and Yan P F. 2002. Information measure for performance of image fusion. Electronics Letters, 38(7): 313-315 [DOI: 10.1049/el:20020212]

Qu L H, Liu S L, Wang M N and Song Z J. 2022. TransMEF: a transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2): 2126-2134 [DOI: 10.1609/aaai.v36i2.20109]

Ranchin T and Wald L. 2000. Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogrammetric Engineering and Remote Sensing, 66(1): 49-61 [DOI: 10.1117/12.748605]

Rao Y J. 1997. In-fibre Bragg grating sensors. Measurement Science and Technology, 8(4): #355 [DOI: 10.1088/0957-0233/8/4/002]

Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, America: IEEE: 779-788 [ DOI: 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ]

Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149 [DOI: 10.1109/TPAMI.2016.2577031]

Roberts J W, Van Aardt J A and Ahmed F B. 2008. Assessment of image fusion procedures using entropy, image quality, and multispectral classification. Journal of Applied Remote Sensing, 2(1): #023522 [DOI: 10.1117/1.2945910]

Ronneberger O, Fischer P and Brox T. 2015. U-net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-assisted Intervention. Munich, Germany: Springer: 234-241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Seo S, Choi J S, Lee J, KimH H, Seo D, Jeong J and Kim M. 2020. UPSNet: unsupervised pan-sharpening network with registration learning between panchromatic and multi-spectral images. IEEE Access, 8: 201199-201217 [DOI: 10.1109/ACCESS.2020.3035802]

Tang L F, Yuan J T and Ma J Y. 2022a. Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network. Information Fusion, 82: 28-42 [DOI: 10.1016/j.inffus.2021.12.004]

Tang L F, Yuan J T, Zhang H, Jiang X Y and Ma J Y. 2022b. PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Information Fusion, 83-84: 79-92 [DOI: 10.1016/j.inffus.2022.03.007]

Tang W, Liu Y, Cheng J, Li C and Chen X. 2021. Green fluorescent protein and phase contrast image fusion via detail preserving cross network. IEEE Transactions on Computational Imaging, 7: 584-597 [DOI: 10.1109/TCI.2021.3083965]

Tang W, Liu Y, Zhang C, Cheng J, Peng H and Chen X. 2019. Green fluorescent protein and phase-contrast image fusion via generative adversarial networks. Computational and Mathematical Methods in Medicine, 2019: #5450373 [DOI: 10.1155/2019/5450373]

Wang J M, Shao Z F, Huang X, Lu T and Zhang R Q. 2022a. A dual-path fusion network for pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 60: 1-14 [DOI: 10.1109/TGRS.2021.3090585]

Wang J M, Shao Z F, Huang X, Lu T, Zhang R Q and Ma J Y. 2021a. Pan-sharpening via high-pass modification convolutional neural network//Proceedings of 2021 IEEE International Conference on Image Processing. Anchorage, USA: IEEE: 1714-1718 [ DOI: 10.1109/ICIP42928.2021.9506568 http://dx.doi.org/10.1109/ICIP42928.2021.9506568 ]

Wang J Z, Xu H N, Wang H F and Yu Z B. 2021. Infrared and visible image fusion based on residual dense block and auto-encoder network. Transactions of Beijing Institute of Technology, 41(10): 1077-1083

王建中, 徐浩楠, 王洪枫, 于子博. 2021. 基于残差密集块和自编码网络的红外与可见光图像融合. 北京理工大学学报, 41(10): 1077-1083 [DOI: 10.15918/j.tbit1001-0645.2021.131]

Wang K P, Zheng M Y, Wei H Y, Qi G Q and Li Y Y. 2020. Multi-modality medical image fusion using convolutional neural network and contrast pyramid. Sensors, 20(8): #2169 [DOI: 10.3390/s20082169]

Wang Y C, Xu S, Liu J M, Zhao Z X, Zhang C X and Zhang J S. 2021b. MFIF-GAN: a new generative adversarial network for multi-focus image fusion. Signal Processing: Image Communication, 96: #116295 [DOI: 10.1016/j.image.2021.116295]

Wang Y J, Xie Y Y, Wu Y Y, Liang K and Qiao J L. 2022b. An unsupervised multi-scale generative adversarial network for remote sensing image pan-sharpening//Proceedings of the 28th International Conference on Multimedia Modeling. Phu Quoc island, Vietnam: Springer: 356-368 [ DOI: 10.1007/978-3-030-98355-0_30 http://dx.doi.org/10.1007/978-3-030-98355-0_30 ]

Wang Z and Bovik A C. 2002. A universal image quality index. IEEE Signal Processing Letters, 9(3): 81-84 [DOI: 10.1109/97.995823]

Wang Z, Bovik A C, Sheikh H R and Simoncelli E P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612 [DOI: 10.1109/TIP.2003.819861]

Wang Z, Simoncelli E P and Bovik A C. 2003. Multiscale structural similarity for imag e quality assessment//Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers. Pacific Grove, USA: IEEE: 1398-1402 [ DOI: 10.1109/ACSSC.2003.1292216 http://dx.doi.org/10.1109/ACSSC.2003.1292216 ]

Xiao B, Wu H F and Bi X L. 2021a. DTMNet: a discrete tchebichef moments-based deep neural network for multi-focus image fusion//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal, Canada: IEEE: 43-51 [ DOI: 10.1109/ICCV48922.2021.00011 http://dx.doi.org/10.1109/ICCV48922.2021.00011 ]

Xiao B, Xu B C, Bi X L and Li W S. 2021b. Global-feature encoding U-Net (GEU-Net) for multi-focus image fusion. IEEE Transactions on Image Processing, 30: 163-175 [DOI: 10.1109/TIP.2020.3033158]

Xu H, Liang P W, Yu W, Jiang J J and Ma J Y. 2019. Learning a generative model for fusing infrared and visible images via conditional generative adversarial network with dual discriminators//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: AAAI Press: 3954-3960 [ DOI: 10.5555/3367471.3367591 http://dx.doi.org/10.5555/3367471.3367591 ]

Xu H and Ma J Y. 2021. EMFusion: an unsupervised enhanced medical image fusion network. Information Fusion, 76: 177-186 [DOI: 10.1016/j.inffus.2021.06.001]

Xu, H, Ma, J Y, Le, Z L, Jiang, J J and Guo, X J. 2020a. Fusiondn: a unified densely connected network for image fusion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(7): 12484-12491 [DOI: 10.1609/aaai.v34i07.6936]

Xu H, Ma J Y, Jiang J J, Guo X J and Ling H B. 2022a. U2Fusion: a unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1): 502-518 [DOI: 10.1109/TPAMI.2020.3012548]

Xu H, Ma J Y, Shao Z F, Zhang H, Jiang J J and Guo X J. 2021a. SDPNet: a deep network for pan-sharpening with enhanced information representation. IEEE Transactions on Geoscience and Remote Sensing, 59(5): 4120-4134 [DOI: 10.1109/TGRS.2020.3022482]

Xu H, Ma J Y, Yuan J T, Le Z L and Liu W. 2022b. RFNet: unsupervised network for mutually reinforcing multi-modal image registration and fusion//Proceedings of 2022 EEE Conference on Computer Vision and Pattern Recognition. New Orleans, USA: IEEE: 19679-19688

Xu H, Ma J Y and Zhang X P. 2020b. MEF-GAN: multi-exposure image fusion via generative adversarial networks. IEEE Transactions on Image Processing, 29: 7203-7216 [DOI: 10.1109/TIP.2020.2999855]

Xu H, Wang X Y and Ma J Y. 2021b. DRF: disentangled representation for visible and infrared image fusion. IEEE Transactions on Instrumentation and Measurement, 70: 1-13 [DOI: 10.1109/TIM.2021.3056645]

Xu H, Zhang H and Ma J Y. 2021c. Classification saliency-based rule for visible and infrared image fusion. IEEE Transactions on Computational Imaging, 7: 824-836 [DOI: 10.1109/TCI.2021.3100986]

Xu S, Ji L Z, Wang Z, LiP F, Sun K, Zhang C X and Zhang J S. 2020c. Towards reducing severe defocus spread effects for multi-focus image fusion via an optimization based strategy. IEEE Transactions on Computational Imaging, 6: 1561-1570 [DOI: 10.1109/TCI.2020.3039564]

Xu S, Wei X L, Zhang C X, Liu J M and Zhang J S. 2020d. MFFW: a new dataset for multi-focus image fusion [EB/OL ] . [2022-02-12 ] . https://arxiv.org/pdf/2002.04780.pdf https://arxiv.org/pdf/2002.04780.pdf

Xu S, Zhang J S, Zhao Z X, Sun K, Liu J M and Zhang C X. 2021d. Deep gradient projection networks for pan-sharpening//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 1366-1375 [ DOI: 10.1109/CVPR46437.2021.00142 http://dx.doi.org/10.1109/CVPR46437.2021.00142 ]

Xydeas C S and Petrović V. 2000. Objective image fusion performance measure. Electronics Letters, 36(4): 308-309 [DOI: 10.1049/el:20000267]

Yan X, Gilani S Z, Qin H L and Mian A. 2020. Structural similarity loss for learning to fuse multi-focus images. Sensors, 20(22): #6647 [DOI: 10.3390/s20226647]

Yang J F, Fu X Y, Hu Y W, Huang Y, Ding X H and Paisley J. 2017. PanNet: a deep network architecture for pan-sharpening//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 1753-1761 [ DOI: 10.1109/ICCV.2017.193 http://dx.doi.org/10.1109/ICCV.2017.193 ]

Yang P, Gao L F and Zi L L. 2021. Image fusion method of convolution sparsity and detail saliency map analysis. Journal of Image and Graphics, 26(10): 2433-2449

杨培, 高雷阜, 訾玲玲. 2021. 卷积稀疏与细节显著图解析的图像融合. 中国图象图形学报, 26(10): 2433-2449 [DOI: 10.11834/jig.200205]

Yang Y, Liu J X, Huang S Y, Wan W G, Wen W Y and Guan J W. 2021a. Infrared and visible image fusion via texture conditional generative adversarial network. IEEE Transactions on Circuits and Systems for Video Technology, 31(12): 4771-4783 [DOI: 10.1109/TCSVT.2021.3054584]

Yang Y, Nie Z P, Huang S Y, Lin P and Wu J H. 2019. Multilevel features convolutional neural network for multifocus image fusion. IEEE Transactions on Computational Imaging, 5(2): 262-273 [DOI: 10.1109/TCI.2018.2889959]

Yang Z G, Chen Y P, Le Z L and Ma Y. 2021b. GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks. Neural Computing and Applications, 33(11): 6133-6145 [DOI: 10.1007/s00521-020-05387-4]

Yin J L, Chen B H and Peng Y T. 2022. Two exposure fusion using prior- aware generative adversarial network. IEEE Transactions on Multimedia, 24: 2841-2851 [DOI: 10.1109/TMM.2021.3089324]

Yin M, Pang J Y, Wei Y Y and Duan P H. 2016. Image fusion algorithm based on nonsubsampled dual-tree complex contourlet transform and compressive sensing pulse coupled neural network. Journal of Computer-Aided Design and Computer Graphics, 28(3): 411-419

殷明, 庞纪勇, 魏远远, 段普宏. 2016. 结合NSDTCT和压缩感知PCNN的图像融合算法. 计算机辅助设计与图形学学报, 28(3): 411-419 [DOI: 10.3969/j.issn.1003-9775.2016.03.005]

Yu L X, Cui Q, Che J, Xu Y L, Zhang F and Li F. 2022. Image fusion model based on structure reparameterization method and spatial attention mechanism. Application Research of Computers, 39(5): 1573-1578, 1600

俞利新, 崔祺, 车军, 许悦雷, 张凡, 李帆. 2022. 结合结构重参数化方法与空间注意力机制的图像融合模型. 计算机应用研究, 39(5): 1573-1578, 1600 [DOI: 10.19734/j.issn.1001-3695.2021.09.0423]

Yuhas R H, Goetz A F H and Boardman J W. 1992. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm [EB/OL ] . [2022-05-18 ] . https://ntrs.nasa.gov/api/citations/19940012238/downloads/19940012238.pdf https://ntrs.nasa.gov/api/citations/19940012238/downloads/19940012238.pdf

Zhang H, Le Z L, Shao Z F, Xu H and Ma J Y. 2021a. MFF-GAN: an unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Information Fusion, 66: 40-53 [DOI: 10.1016/j.inffus.2020.08.022]

Zhang H and Ma J Y. 2021a. GTP-PNet: a residual learning network based on gradient transformation prior for pansharpening. ISPRS Journal of Photogrammetry and Remote Sensing, 172: 223-239 [DOI: 10.1016/j.isprsjprs.2020.12.014]

Zhang H and Ma J Y. 2021b. SDNet: a versatile squeeze-and-decomposition network for real-time image fusion. International Journal of Computer Vision, 129(10): 2761-2785 [DOI: 10.1007/s11263-021-01501-8]

Zhang H, Ma J Y, Chen C and Tian X. 2020a. NDVI-Net: a fusion network for generating high-resolution normalized difference vegetation index in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 168: 182-196 [DOI: 10.1016/j.isprsjprs.2020.08.010]

Zhang H, Xu H, Tian X, Jiang J J and Ma J Y. 2021b. Image fusion meets deep learning: a survey and perspective. Information Fusion, 76: 323-336 [DOI: 10.1016/j.inffus.2021.06.008]

Zhang H, Xu H, Xiao Y, Guo X J and Ma J Y. 2020b. Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. Proceedings of 2020 AAAI Conference on Artificial Intelligence, 34(7): 12797-12804 [DOI: 10.1609/AAAI.v34i07.6975]

Zhang H, Yuan J T, Tian X and Ma J Y. 2021c. GAN-FM: infrared and visible image fusion using gan with full-scale skip connection and dual markovian discriminators. IEEE Transactions on Computational Imaging, 7: 1134-1147 [DOI: 10.1109/TCI.2021.3119954]

Zhang Q and Maldague X. 2016. An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing. Infrared Physics and Technology, 74: 11-20 [DOI: 10.1016/j.infrared.2015.11.003]

Zhang X C. 2021. Benchmarking and comparing multi-exposure image fusion algorithms. Information Fusion, 74: 111-131 [DOI: 10.1016/j.inffus.2021.02.005]

Zhang X C. 2022. Deep learning-based multi-focus image fusion: a survey and a comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9): 4819-4838 [DOI: 10.1109/TPAMI.2021.3078906]

Zhang X C, Ye P and Xiao G. 2020c. VIFB: a visible and infrared image fusion benchmark//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, USA: IEEE: 468-478 [ DOI: 10.1109/CVPRW50498.2020.00060 http://dx.doi.org/10.1109/CVPRW50498.2020.00060 ]

Zhang Y, Liu Y, Sun P, Yan H, Zhao X L and Zhang L. 2020d. IFCNN: a general image fusion framework based on convolutional neural network. Information Fusion, 54: 99-118 [DOI: 10.1016/j.inffus.2019.07.011]

Zhang Y M and Ji Q. 2005. Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5): 699-714 [DOI: 10.1109/TPAMI.2005.93]

Zhao C, Wang T F and Lei B Y. 2021a. Medical image fusion method based on dense block and deep convolutional generative adversarial network. Neural Computing and Applications, 33(12): 6595-6610 [DOI: 10.1007/s00521-020-05421-5]

Zhao F, Zhao W D, Lu H M, Liu Y, Yao L B and Liu Y. 2021b. Depth-distilled multi-focus image fusion. IEEE Transactions on Multimedia [DOI: 10.1109/TMM.2021.3134565]

Zhou H B, Wu W, Zhang Y D, Ma J Y and Ling H B. 2021. Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network. IEEE Transactions on Multimedia [DOI: 10.1109/TMM.2021.3129609]

Zhou H Y, Liu Q J, Weng D W and Wang Y H. 2022. Unsupervised cycle-consistent generative adversarial networks for pan sharpening. IEEE Transactions on Geoscience and Remote Sensing, 60: #5408814 [DOI: 10.1109/TGRS.2022.3166528]

Zhou J, Civco D L and Silander J A. 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19(4): 743-757 [DOI: 10.1080/014311698215973]

Zhou Y N and Yang X M. 2021. Infrared and visible image fusion based on GAN. Modern Computer, 27(16): 94-97

周祎楠, 杨晓敏. 2021. 基于GAN的红外与可见光图像融合算法. 现代计算机, 27(16): 94-97 [DOI: 10.3969/j.issn.1007-1423.2021.16.020]

Zhou Y W, Yang P L, Chen Q and Sun Q S. 2015. Pan-sharpening model based on MTF and variational method. Acta Automatica Sinica, 41(2): 342-352

周雨薇, 杨平吕, 陈强, 孙权森. 2015. 基于MTF和变分的全色与多光谱图像融合模型. 自动化学报, 41(2): 342-352 [DOI: 10.16383/j.aas.2015.c140121]

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