Awesome
图像融合(Image Fusion)
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[2024-07-16] 我们的论文《DRMF: Degradation-Robust Multi-Modal Image Fusion via Composable Diffusion Prior》被《ACM MM 2024》正式接收！[论文下载] [Code]
[2023-06-05] 我们的论文《Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity》被《Information Fusion》正式接收！[论文下载] [Code]
[2022-07-29] 我们的综述论文《基于深度学习的图像融合方法综述》被《中国图象图形学报》正式接收！[论文下载]
- Citation
- 多模图像融合(Multi-Modal Image Fusion)
- 数字摄影图像融合(Digital Photography Image Fusion)
- 遥感影像融合(Remote Sensing Image Fusion)
- 通用图像融合框架(General Image Fusion Framerwork)
- 综述(Survey)
- 数据集(Dataset)
- 评估指标(Evaluation Metric)
Citation
如果我们的总结对你有所帮助, 请引用以下论文：
@article{Tang2022Survey,
title={Deep learning-based image fusion: A survey},
author={Tang, Linfeng and Zhang, Hao and Xu, Han and Ma, Jiayi},
journal={Journal of Image and Graphics}
volume={28},
number={1},
pages={3--36},
year={2023}
}
@inproceedings{Tang2024DRMF,
title={DRMF: Degradation-Robust Multi-Modal Image Fusion via Composable Diffusion Prior},
author={Tang, Linfeng and Deng, Yuxin and Yi, Xunpeng and Yan, Qinglong and Yuan, Yixuan and Ma, Jiayi},
booktitle={Proceedings of the ACM International Conference on Multimedia},
pages={8546--8555},
year={2024}
}
@article{Tang2024CAMF,
title={CAMF: An Interpretable Infrared and Visible Image Fusion Network Based on Class Activation Mapping},
author={Tang, Linfeng and Chen, Ziang and Huang, Jun and Ma, Jiayi},
journal={IEEE Transactions on Multimedia},
year={2024},
volume={26},
pages={4776-4791},
publisher={IEEE}
}
@article{Tang2022SuperFusion,
title={SuperFusion: A versatile image registration and fusion network with semantic awareness},
author={Tang, Linfeng and Deng, Yuxin and Ma, Yong and Huang, Jun and Ma, Jiayi},
journal={IEEE/CAA Journal of Automatica Sinica},
volume={9},
number={12},
pages={2121--2137},
year={2022},
publisher={IEEE}
}
@article{Ma2022SwinFusion,
title={SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer},
author={Ma, Jiayi and Tang, Linfeng and Fan, Fan and Huang, Jun and Mei, Xiaoguang and Ma, Yong},
journal={IEEE/CAA Journal of Automatica Sinica},
volume={9},
number={7},
pages={1200--1217},
year={2022},
publisher={IEEE}
}
@article{TangSeAFusion,
title = {Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network},
author = {Linfeng Tang and Jiteng Yuan and Jiayi Ma},
journal = {Information Fusion},
volume = {82},
pages = {28-42},
year = {2022},
issn = {1566-2535},
publisher={Elsevier}
}
@article{Tang2022DIVFusion,
title={DIVFusion: Darkness-free infrared and visible image fusion},
author={Tang, Linfeng and Xiang, Xinyu and Zhang, Hao and Gong, Meiqi and Ma, Jiayi},
journal={Information Fusion},
volume = {91},
pages = {477-493},
year = {2023},
publisher={Elsevier}
}
@article{Tang2022PIAFusion,
title={PIAFusion: A progressive infrared and visible image fusion network based on illumination aware},
author={Tang, Linfeng and Yuan, Jiteng and Zhang, Hao and Jiang, Xingyu and Ma, Jiayi},
journal={Information Fusion},
volume = {83-84},
pages = {79-92},
year = {2022},
issn = {1566-2535},
publisher={Elsevier}
}
@article{Ma2021STDFusionNet,
title={STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection},
author={Jiayi Ma, Linfeng Tang, Meilong Xu, Hao Zhang, and Guobao Xiao},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2021},
volume={70},
number={},
pages={1-13},
doi={10.1109/TIM.2021.3075747}，
publisher={IEEE}
}
多模图像融合(Multi-Modal Image Fusion)
红外和可见光图像融合(Infrared and visible image fusion)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>发表年份</th> </tr> </thead> <tbody> <tr> <td>DenseFuse</td> <td>DenseFuse: A Fusion Approach to Infrared and Visible Images</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8580578/">Paper</a></td> <td><a href="https://github.com/hli1221/imagefusion_densefuse">Code</a></td> <td>TIP</td> <td>AE</td> <td>自监督</td> <td>2019</td> </tr> <tr> <td>FusionGAN</td> <td>FusionGAN: A generative adversarial network for infrared and visible image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253518301143%3Fvia%253Dihub">Paper</a></td> <td><a href="https://github.com/jiayi-ma/FusionGAN">Code</a></td> <td>InfFus</td> <td>GAN</td> <td>无监督</td> <td>2019</td> </tr> <tr> <td>DDcGAN</td> <td>Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators</td> <td><a href="https://www.ijcai.org/proceedings/2019/0549.pdf">Paper</a></td> <td><a href="https://github.com/hanna-xu/DDcGAN">Code</a></td> <td>IJCAI</td> <td>GAN</td> <td>无监督</td> <td>2019</td> </tr> <tr> <td>NestFuse</td> <td>NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models</td> <td><a href="https://ieeexplore.ieee.org/document/9127964/">Paper</a></td> <td><a href="https://github.com/hli1221/imagefusion-nestfuse">Code</a></td> <td>TIM</td> <td>AE</td> <td>自监督</td> <td>2020</td> </tr> <tr> <td>DDcGAN</td> <td>DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9031751/">Paper</a></td> <td><a href="https://github.com/hanna-xu/DDcGAN">Code</a></td> <td>TIP </td> <td>GAN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>DIDFuse</td> <td>DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion</td> <td><a href="https://arxiv.org/abs/2003.09210">Paper</a></td> <td><a href="https://github.com/Zhaozixiang1228/IVIF-DIDFuse">Code</a></td> <td>IJCAI </td> <td>AE</td> <td>自监督</td> <td>2020</td> </tr> <tr> <td>RFN-Nest</td> <td>RFN-Nest: An end-to-end residual fusion network for infrared and visible images</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253521000440%3Fvia%253Dihub">Paper</a></td> <td><a href="https://github.com/hli1221/imagefusion-rfn-nest">Code</a></td> <td>InfFus</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>CSF</td> <td>Classification Saliency-Based Rule for Visible and Infrared Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9502544">Paper</a></td> <td><a href="https://github.com/hanna-xu/CSF">Code</a></td> <td>TCI</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>DRF</td> <td>DRF: Disentangled Representation for Visible and Infrared Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9345717/">Paper</a></td> <td><a href="https://github.com/hanna-xu/DRF">Code</a></td> <td>TIM</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>SEDRFuse</td> <td>SEDRFuse: A Symmetric Encoder–Decoder With Residual Block Network for Infrared and Visible Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9187663">Paper</a></td> <td><a href="https://github.com/jianlihua123/SEDRFuse">Code</a></td> <td>TIM</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>MFEIF</td> <td>Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9349250">Paper</a></td> <td></td> <td>TCSVT</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>Meta-Learning</td> <td>Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9394791/">Paper</a></td> <td></td> <td>TIP</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>RXDNFuse</td> <td>RXDNFuse: A aggregated residual dense network for infrared and visible image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253520304152%3Fvia%253Dihub">Paper</a></td> <td><a href="https://github.com/JinyuanLiu-CV/MFEIF">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>STDFusionNet</td> <td>STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection</td> <td><a href="https://ieeexplore.ieee.org/document/9416507">Paper</a></td> <td><a href="https://github.com/Linfeng-Tang/STDFusionNet">Code</a></td> <td>TIM</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>D2LE</td> <td>A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9293146">Paper</a></td> <td></td> <td>TIP</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>HAF</td> <td>Searching a Hierarchically Aggregated Fusion Architecture for Fast Multi-Modality Image Fusion</td> <td><a href="https://dl.acm.org/doi/abs/10.1145/3474085.3475299%3Fcasa_token%3DtT48gSwVWjkAAAAA%3AQaDUAB7nLzWcByiAESzOTAgFjdh5kLxS8J612shuDn3RLLIOcU1AX7AhcvYT9UlGTub1mi85Tws">Paper</a></td> <td><a href="https://github.com/LiuzhuForFun/Hierarchical-NAS-Image-Fusion">Code</a></td> <td>ACM MM</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>SDDGAN</td> <td>Semantic-supervised Infrared and Visible Image Fusion via a Dual-discriminator Generative Adversarial Network</td> <td><a href="https://ieeexplore.ieee.org/document/9623476">Paper</a></td> <td><a href="https://github.com/WeiWu-WIT/SDDGAN">Code</a></td> <td>TMM</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>Detail-GAN</td> <td>Infrared and visible image fusion via detail preserving adversarial learning</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253519300314%3Fvia%253Dihub">Paper</a></td> <td><a href="https://github.com/jiayi-ma/ResNetFusion">Code</a></td> <td>InfFus</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>Perception-GAN</td> <td>Image fusion based on generative adversarial network consistent with perception</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253521000439%3Fcasa_token%3D-LrGkXT12IMAAAAA%3A35WH5dIx-2zWPZ3sTFL3cgjjCK_17nkc0xYVkDXL8Pp51k3DIPtaNT8NWvuKoXlIOoRkKi87AZo0">Paper</a></td> <td><a href="https://github.com/thfylsty/imagefusion_Perceptual_FusionGan">Code</a></td> <td>InfFus</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>GAN-FM</td> <td>GAN-FM: Infrared and Visible Image Fusion Using GAN With Full-Scale Skip Connection and Dual Markovian Discriminators</td> <td><a href="https://ieeexplore.ieee.org/document/9573457">Paper</a></td> <td><a href="https://github.com/yuanjiteng/GAN-FM">Code</a></td> <td>TCI</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>AttentionFGAN</td> <td>AttentionFGAN: Infrared and Visible Image Fusion Using Attention-Based Generative Adversarial Networks</td> <td><a href="https:////ieeexplore.ieee.org/document/9103116/">Paper</a></td> <td></td> <td>TMM</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>GANMcC</td> <td>GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9274337">Paper</a></td> <td><a href="https://github.com/jiayi-ma/GANMcC">Code</a></td> <td>TIM</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>MgAN-Fuse</td> <td>Multigrained Attention Network for Infrared and Visible Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9216075">Paper</a></td> <td></td> <td>TIM</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>TC-GAN</td> <td>Infrared and Visible Image Fusion via Texture Conditional Generative Adversarial Network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9335976">Paper</a></td> <td></td> <td>TCSVT</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>AUIF</td> <td>Efficient and model-based infrared and visible image fusion via algorithm unrolling</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9416456/">Paper</a></td> <td><a href="https://github.com/Zhaozixiang1228/IVIF-AUIF-Net">Code</a></td> <td>TCSVT</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>TarDAL</td> <td>Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection</td> <td><a href="https://arxiv.org/abs/2203.16220v1">Paper</a></td> <td><a href="https://github.com/JinyuanLiu-CV/TarDAL">Code</a></td> <td>CVPR</td> <td>GAN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>RFNet</td> <td>RFNet: Unsupervised Network for Mutually Reinforcing Multi-modal Image Registration and Fusion</td> <td><a href="https://www.researchgate.net/publication/360238557_RFNet_Unsupervised_Network_for_Mutually_Reinforcing_Multi-modal_Image_Registration_and_Fusion">Paper</a></td> <td><a href="https://github.com/hanna-xu">Code</a></td> <td>CVPR</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>SeAFusion</td> <td>Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253521002542%3Fvia%253Dihub">Paper</a></td> <td><a href="https://github.com/Linfeng-Tang/SeAFusion">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>PIAFusion</td> <td>PIAFusion: A progressive infrared and visible image fusion network based on illumination aware</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S156625352200032X">Paper</a></td> <td><a href="https://github.com/Linfeng-Tang/PIAFusion" target="_blank" rel="noopener noreferrer">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>UMF-CMGR</td> <td>Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration</td> <td><a href="https://arxiv.org/pdf/2205.11876.pdf">Paper</a></td> <td><a href="https://github.com/wdhudiekou/UMF-CMGR" target="_blank" rel="noopener noreferrer">Code</a></td> <td>IJCAI</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>DetFusion</td> <td>DetFusion: A Detection-driven Infrared and Visible Image Fusion Network</td> <td><a href="https://dl.acm.org/doi/abs/10.1145/3503161.3547902?casa_token=YmZKEkd1zVcAAAAA:wb0HbfopS60BwSh0_QAdHHjwNQ5ZoWxduwHfBd1NzLbMr32AOPcInC4TCbZ5KM9Ly1PbVIGkmg">Paper</a></td> <td><a href="https://github.com/SunYM2020/DetFusion" target="_blank" rel="noopener noreferrer">Code</a></td> <td>ACM MM</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>DIVFusion</td> <td>DIVFusion: Darkness-free infrared and visible image fusion</td> <td><a href="https://authors.elsevier.com/c/1g4EB_ZdCkUn0I">Paper</a></td> <td><a href="https://github.com/Xinyu-Xiang/DIVFusion" target="_blank" rel="noopener noreferrer">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2023</td> </tr> <tr> <td>PSFusion</td> <td>Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253523001860">Paper</a></td> <td><a href="https://github.com/Linfeng-Tang/PSFusion" target="_blank" rel="noopener noreferrer">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2023</td> </tr> </tbody> </table>医学图像融合(Medical image fusion)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>CNN</td> <td>A medical image fusion method based on convolutional neural networks</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8009769/">Paper</a></td> <td></td> <td>ICIF</td> <td>CNN</td> <td>无监督</td> <td>2017</td> </tr> <tr> <td>Zero-LMF</td> <td>Zero-Learning Fast Medical Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9011178">Paper</a></td> <td><a href="https://github.com/PanPapag/Zero-Learning-Fast-Medical-Image-Fusion">Code</a></td> <td>ICIF</td> <td>CNN</td> <td>无监督</td> <td>2019</td> </tr> <tr> <td>DDcGAN</td> <td>Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators</td> <td><a href="https://www.ijcai.org/proceedings/2019/0549.pdf">Paper</a></td> <td><a href="https://github.com/hanna-xu/DDcGAN">Code</a></td> <td>IJCAI</td> <td>GAN</td> <td>无监督</td> <td>2019</td> </tr> <tr> <td>GFPPC-GAN</td> <td>Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative Adversarial Networks</td> <td><a href="https://www.hindawi.com/journals/cmmm/2019/5450373/">Paper</a></td> <td></td> <td>CMMM</td> <td>GAN</td> <td>无监督</td> <td>2019</td> </tr> <tr> <td>CCN-CP</td> <td>Multi-modality medical image fusion using convolutional neural network and contrast pyramid</td> <td><a href="https://www.mdpi.com/1424-8220/20/8/2169">Paper</a></td> <td></td> <td>Sensors</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>DDcGAN</td> <td>DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9502544/">Paper</a></td> <td><a href="https://github.com/hanna-xu/DDcGAN">Code</a></td> <td>TIP </td> <td>GAN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>MGMDcGAN</td> <td>Medical Image Fusion Using Multi-Generator Multi-Discriminator Conditional Generative Adversarial Network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9042283/">Paper</a></td> <td><a href="https://github.com/HaoZhang1018/MGMDcGAN">Code</a></td> <td>Access </td> <td>GAN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>D2LE</td> <td>A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9293146">Paper</a></td> <td></td> <td>TIP </td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>HAF</td> <td>Searching a Hierarchically Aggregated Fusion Architecture for Fast Multi-Modality Image Fusion</td> <td><a href="https://dl.acm.org/doi/abs/10.1145/3474085.3475299?casa_token=tT48gSwVWjkAAAAA:QaDUAB7nLzWcByiAESzOTAgFjdh5kLxS8J612shuDn3RLLIOcU1AX7AhcvYT9UlGTub1mi85Tws">Paper</a></td> <td><a href="https://github.com/LiuzhuForFun/Hierarchical-NAS-Image-Fusion">Code</a></td> <td>ACM MM</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>EMFusion</td> <td>EMFusion: An unsupervised enhanced medical image fusion network</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253521001275?casa_token=8Z3_V5mzH4YAAAAA:8nFKQlHpIrI_P965NSYYEL_dAYRFrjeASLO3ZPK2BXwGRW60yrszasTBxgCckwhGn987UyWjxhI2">Paper</a></td> <td><a href="https://github.com/hanna-xu/EMFusion">Code</a></td> <td>InfFus</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>DPCN-Fusion</td> <td>Green Fluorescent Protein and Phase Contrast Image Fusion Via Detail Preserving Cross Network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9442298">Paper</a></td> <td><a href="https://github.com/yuliu316316/DPCN-Fusion">Code</a></td> <td>TCI</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>MSPRN</td> <td>A multiscale residual pyramid attention network for medical image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1746809421000859?casa_token=rNLcNhcKwrwAAAAA:aHLHOTkdOzEqlYEDDBhX0PjLQhG0Sxel6u--8l72Ok4bJeFJPA8iuPSJeIGtYxM9aNAyBoW2fcxw">Paper</a></td> <td><a href="https://github.com/jeffsonfu/MSRPAN">Code</a></td> <td>BSPC</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>DCGAN</td> <td>Medical image fusion method based on dense block and deep convolutional generative adversarial network</td> <td><a href="https://link.springer.com/article/10.1007/s00521-020-05421-5">Paper</a></td> <td></td> <td>NCA</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> </tbody> </table>数字摄影图像融合(Digital Photography Image Fusion)
多曝光图像融合(Multi-exposure image fusion)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>DeepFuse</td> <td>DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs</td> <td><a href="https://openaccess.thecvf.com/content_ICCV_2017/papers/Prabhakar_DeepFuse_A_Deep_ICCV_2017_paper.pdf">Paper</a></td> <td><a href="https://github.com/hli1221/Imagefusion_deepfuse">Code</a></td> <td>ICCV</td> <td>CNN</td> <td>无监督</td> <td>2017</td> </tr> <tr> <td>CNN</td> <td>Multi-exposure fusion with CNN features</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8451689/">Paper</a></td> <td><a href="https://github.com/xiaohuiben/MEF-CNN-feature">Code</a></td> <td>ICIP</td> <td>CNN</td> <td>无监督</td> <td>2018</td> </tr> <tr> <td>MEF-Net</td> <td>Deep guided learning for fast multi-exposure image fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8906233">Paper</a></td> <td><a href="https://github.com/makedede/MEFNet">Code</a></td> <td>TIP</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>ICEN</td> <td>Multi-exposure high dynamic range imaging with informative content enhanced network</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S0925231219318089?casa_token=jbVTcQ7pOcoAAAAA:YDp0aWea0SCJaY8AstIN4r0PO--bL-3fxkidb41bEHXatep0PDGo5MvHY7Gtt-gLIR8yJEcFgcs">Paper</a></td> <td></td> <td>NC</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>MEF-GAN</td> <td>MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9112609">Paper</a></td> <td><a href="https://github.com/jiayi-ma/MEF-GAN">Code</a></td> <td>TIP</td> <td>GAN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>CF-Net</td> <td>Deep coupled feedback network for joint exposure fusion and image super-resolutions</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9357931">Paper</a></td> <td><a href="https://github.com/ytZhang99/CF-Net">Code</a></td> <td>TIP</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>UMEF</td> <td>Deep unsupervised learning based on color un-referenced loss functions for multi-exposure image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253520303419?casa_token=w8HTDPj2GWwAAAAA:GS6sF3Sx8aYvm4-k24DcEw59Jdci-r2dyfiPPOiLHN916-ZTI2VOaoSIeq1u8eUmg_JyLlYuCQ">Paper</a></td> <td><a href="https://github.com/cathying-cq/UMEF">Code</a></td> <td>InFus</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>PA-AGN</td> <td>Two exposure fusion using prior-aware generative adversarial network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9456042">Paper</a></td> <td></td> <td>TMM</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>AGAL</td> <td>Attention-guided Global-local Adversarial Learning for Detail-preserving Multi-exposure Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9684913">Paper</a></td> <td><a href="https://github.com/JinyuanLiu-CV/AGAL">Code</a></td> <td>TCSVT</td> <td>GAN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>GANFuse</td> <td>GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks</td> <td><a href="https://link.springer.com/article/10.1007/s00521-020-05387-4">Paper</a></td> <td></td> <td>NCAA</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>DRLF</td> <td>Automatic Intermediate Generation With Deep Reinforcement Learning for Robust Two-Exposure Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9466369/">Paper</a></td> <td></td> <td>TNNLS</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>Trans-MEF</td> <td>TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning</td> <td><a href="https://arxiv.org/pdf/2112.01030.pdf">Paper</a></td> <td><a href="https://github.com/miccaiif/TransMEF">Code</a></td> <td>AAAI</td> <td>AE</td> <td>自监督</td> <td>2022</td> </tr> <tr> <td>DPE-MEF</td> <td>Multi-exposure image fusion via deep perceptual enhancement</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253521002049?casa_token=9wOlURjhP7cAAAAA:7tMeJQtO6qVFoih_JlT4KVl1sPRInc-cLDSbe9W7474gyqG-O11XWbvlGZanRj8ZVw-9rmh88B34">Paper</a></td> <td><a href="https://github.com/dongdong4fei/DPE-MEF">Code</a></td> <td>InFus</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> </tbody> </table>多聚焦图像融合(Multi-focus image fusion)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>CNN</td> <td>Multi-focus image fusion with a deep convolutional neural network</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253516302081?casa_token=ExizHHOx2PYAAAAA:irheSncDJuLbBmd2bcejDxW9Cjf9Y9WRMUFUjfhG2DgFuY2Nni1qTg1AqwKNSsXUVBj7dFKTIQ">Paper</a></td> <td><a href="https://github.com/xiaohuiben/MEF-CNN-feature">Code</a></td> <td>InFus</td> <td>CNN</td> <td>有监督</td> <td>2017</td> </tr> <tr> <td>ECNN</td> <td>Ensemble of CNN for multi-focus image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253518306043?casa_token=p1rP_1bKz_YAAAAA:X75eWD_lFNZeNxpzXASehge1d0sptXBvDH-R9aAURPSF-AKRyUYIWtiyvf4m5j1UlF895TRlvFXm">Paper</a></td> <td><a href="https://github.com/mostafaaminnaji/ECNN">Code</a></td> <td>InFus</td> <td>CNN</td> <td>有监督</td> <td>2019</td> </tr> <tr> <td>MLFCNN</td> <td>Multilevel features convolutional neural network for multifocus image fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8599008">Paper</a></td> <td></td> <td>TCI</td> <td>CNN</td> <td>有监督</td> <td>2019</td> </tr> <tr> <td>DRPL</td> <td>DRPL: Deep Regression Pair Learning for Multi-Focus Image Fusion </td> <td><a href="https://ieeexplore.ieee.org/document/9020016">Paper</a></td> <td><a href="https://github.com/sasky1/DPRL">Code</a></td> <td>TIP</td> <td>CNN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>MMF-Net</td> <td>An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-Focus Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9178463">Paper</a></td> <td><a href="https://github.com/xytmhy/MMF-Net-Multi-Focus-Image-Fusion">Code</a></td> <td>TCI</td> <td>CNN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>MFF-SSIM</td> <td>Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy</td> <td><a href="https://ieeexplore.ieee.org/document/9269377">Paper</a></td> <td><a href="https://github.com/shuangxu96/MFF-SSIM">Code</a></td> <td>Sensors</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>MFNet</td> <td>Structural Similarity Loss for Learning to Fuse Multi-Focus Images</td> <td><a href="https://www.mdpi.com/1424-8220/20/22/6647">Paper</a></td> <td></td> <td>TIP</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>GEU-Net</td> <td>Global-Feature Encoding U-Net (GEU-Net) for Multi-Focus Image Fusion [GEU-Net</td> <td><a href="https://ieeexplore.ieee.org/document/9242278">Paper</a></td> <td><a href="https://github.com/xudif/GEU-Net">Code</a></td> <td>TCI</td> <td>CNN</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>DTMNet</td> <td>DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion</td> <td><a href="https://openaccess.thecvf.com/content/ICCV2021/html/Xiao_DTMNet_A_Discrete_Tchebichef_Moments-Based_Deep_Neural_Network_for_Multi-Focus_ICCV_2021_paper.html">Paper</a></td> <td></td> <td>TMM</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>SMFuse</td> <td> SMFuse: Multi-Focus Image Fusion Via Self-Supervised Mask-Optimization</td> <td><a href="https://ieeexplore.ieee.org/document/9369892">Paper</a></td> <td><a href="https://github.com/jiayi-ma/SMFuse">Code</a></td> <td>NCA</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>ACGAN</td> <td>A generative adversarial network with adaptive constraints for multi-focus image fusion</td> <td><a href="https://link.springer.com/article/10.1007/s00521-020-04863-1">Paper</a></td> <td><a href="https://github.com/LeBoyal/NCAA_code">Code</a></td> <td>ICCV</td> <td>GAN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>FuseGAN</td> <td>Learning to fuse multi-focus image via conditional generative adversarial network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8625482">Paper</a></td> <td></td> <td>TIP</td> <td>GAN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>D2FMIF</td> <td>Depth-Distilled Multi-focus Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/document/9647937">Paper</a></td> <td></td> <td>TMM</td> <td>CNN</td> <td>有监督</td> <td>2019</td> </tr> <tr> <td>SESF-Fuse</td> <td>SESF-Fuse: an unsupervised deep model for multi-focus image fusion</td> <td><a href="https://link.springer.com/article/10.1007/s00521-020-05358-">Paper</a></td> <td><a href="https://github.com/Keep-Passion/SESF-Fuse">Code</a></td> <td>NCAA</td> <td>CNN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>MFF-GAN</td> <td>MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253520303572?casa_token=GA0lIhkd-_IAAAAA:xjU-avtOOmTraORf7bZZ5sNwcWgccV_EFA6bFjpA05PyFwxQZq2Giil2aJN_l0FYa0JghBiBlufg">Paper</a></td> <td><a href="https://github.com/HaoZhang1018/MFF-GAN">Code</a></td> <td>InFus</td> <td>GAN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>MFIF-GAN</td> <td>MFIF-GAN: A new generative adversarial network for multi-focus image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S0923596521001260?casa_token=FouAKgZYaXcAAAAA:zj3zevZjxqVSXpMwFcDoYPBXtrnAorpm5SkdXeDK8A93ob4QZkZeDpJgeRZtiS30Yj7Id1-c3gfS">Paper</a></td> <td><a href="https://github.com/ycwang-libra/MFIF-GAN">Code</a></td> <td>SPIC</td> <td>GAN</td> <td>有监督</td> <td>2021</td> </tr> </tbody> </table>遥感影像融合(Remote Sensing Image Fusion)
全色图像锐化(Pansharpening)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>PNN</td> <td>Pansharpening by Convolutional Neural Networks</td> <td><a href="https://www.mdpi.com/2072-4292/8/7/594">Paper</a></td> <td><a href="https://github.com/ThomasWangWeiHong/Pansharpening-by-Convolutional-Neural-Network">Code</a></td> <td>RS</td> <td>CNN</td> <td>有监督</td> <td>2016</td> </tr> <tr> <td>PanNet</td> <td>PanNet: A deep network architecture for pan-sharpening</td> <td><a href="https://openaccess.thecvf.com/content_iccv_2017/html/Yang_PanNet_A_Deep_ICCV_2017_paper.html">Paper</a></td> <td><a href="https://github.com/oyam/PanNet-Landsat">Code</a></td> <td>PanNet</td> <td>CNN</td> <td>有监督</td> <td>2017</td> </tr> <tr> <td>TFNet</td> <td>Remote sensing image fusion based on two-stream fusion network</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253517308060?casa_token=67RmX5_5pIsAAAAA:hbrOWj2JUylmwMrB2-MtynJXXS2av8lI_NbTB1-z9gM_oPKVlRS2XQUkciVUoDUYU3Sa3e5JJ7fL">Paper</a></td> <td><a href="https://github.com/liouxy/tfnet_pytorch">Code</a></td> <td>TFNet</td> <td>CNN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>BKL</td> <td>Unsupervised Blur Kernel Learning for Pansharpening</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9324543/">Paper</a></td> <td></td> <td>IGARSS</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>Pan-GAN</td> <td>Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253520302591?casa_token=ERMiphW48jIAAAAA:PgpCOB6ClGnMhrXJHFC-h3riITCdpQ7sPePX7xEtlDzOOjShCWwut66VMvQ3j8hCHjUupsiAW7Ca)%5d%20%5bCode%5d(https://github.com/yuwei998/PanGAN">Paper</a></td> <td><a href="https://github.com/yuwei998/PanGAN">Code</a></td> <td>InFus</td> <td>GAN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>UCNN</td> <td>Pansharpening via Unsupervised Convolutional Neural Networks</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9136909/">Paper</a></td> <td></td> <td>JSTARS</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>UPSNet</td> <td>UPSNet: Unsupervised Pan-Sharpening Network With Registration Learning Between Panchromatic and Multi-Spectral Images</td> <td><a href="https://ieeexplore.ieee.org/document/9248047/">Paper</a></td> <td></td> <td>ACCESS</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>GPPNN</td> <td>Deep Gradient Projection Networks for Pan-sharpening</td> <td><a href="https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Deep_Gradient_Projection_Networks_for_Pan-sharpening_CVPR_2021_paper.html">Paper</a></td> <td><a href="https://github.com/shuangxu96/GPPNN">Code</a></td> <td>CVPR</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>GTP-PNet</td> <td>GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S092427162030352X?casa_token=MpRVtfAoM8QAAAAA:0MxRCKHqREmjpngbHdN-EiTfNNbpVBikgP67VePg9hyFqm72Ot2NuRg1BeeslNmd0pVRsisi5niD">Paper</a></td> <td><a href="https://github.com/HaoZhang1018/GTP-PNet">Code</a></td> <td>ISPRS</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>HMCNN</td> <td>Pan-Sharpening Via High-Pass Modification Convolutional Neural Network</td> <td><a href="https://github.com/jiaming-wang/HMB">Paper</a></td> <td><a href="https://github.com/xiaohuiben/MEF-CNN-feature">Code</a></td> <td>ICIP</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>SDPNet</td> <td>SDPNet: A Deep Network for Pan-Sharpening With Enhanced Information Representation</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9200533/">Paper</a></td> <td><a href="https://github.com/hanna-xu/SDPNet-for-pansharpening">Code</a></td> <td>TGRS</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>SIPSA-Net</td> <td>SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery</td> <td><a href="https://openaccess.thecvf.com/content/CVPR2021/html/Lee_SIPSA-Net_Shift-Invariant_Pan_Sharpening_With_Moving_Object_Alignment_for_Satellite_CVPR_2021_paper.html">Paper</a></td> <td><a href="https://github.com/brachiohyup/SIPSA">Code</a></td> <td>CVPR</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>SRPPNN</td> <td>Super-resolution-guided progressive pansharpening based on a deep convolutional neural network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9172104">Paper</a></td> <td><a href="https://github.com/MrCPlusPlus/SRPPNN">Code</a></td> <td>TGRS</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>PSGAN</td> <td>PSGAN: A generative adversarial network for remote sensing image pan-sharpening</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9306912">Paper</a></td> <td><a href="https://github.com/zhysora/PSGan-Family">Code</a></td> <td>TGRS</td> <td>GAN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>MDCNN</td> <td>MDCNN: multispectral pansharpening based on a multiscale dilated convolutional neural network</td> <td><a href="https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-15/issue-3/036516/MDCNN--multispectral-pansharpening-based-on-a-multiscale-dilated-convolutional/10.1117/1.JRS.15.036516.short?SSO=1">Paper</a></td> <td></td> <td>JRS</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>LDP-Net</td> <td>LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes</td> <td><a href="https://arxiv.org/abs/2111.12483">Paper</a></td> <td><a href="https://github.com/suifenglian/LDP-Net">Code</a></td> <td>Arxiv</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>DIGAN</td> <td>Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S0957417421013440?casa_token=UH2brpdrn4MAAAAA:yh_OVkvU5Lg5r6bv5cKFXIZNR4PS8Q6SMq-tjj4Wcs4Bekpon4fnSzocnLnZzQYX-066svxzb9w">Paper</a></td> <td></td> <td>ESA</td> <td>GAN</td> <td>有监督</td> <td>2022</td> </tr> <tr> <td>DPFN</td> <td>A Dual-Path Fusion Network for Pan-Sharpening</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9467620/">Paper</a></td> <td><a href="https://github.com/jiaming-wang/DPFN">Code</a></td> <td>TGRS</td> <td>CNN</td> <td>有监督</td> <td>2022</td> </tr> <tr> <td>MSGAN</td> <td>An Unsupervised Multi-scale Generative Adversarial Network for Remote Sensing Image Pan-Sharpening</td> <td><a href="https://link.springer.com/chapter/10.1007/978-3-030-98355-0_30">Paper</a></td> <td></td> <td>ICMM</td> <td>GAN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>UCGAN</td> <td>Unsupervised Cycle-Consistent Generative Adversarial Networks for Pan Sharpening</td> <td><a href="https://ieeexplore.ieee.org/document/9755137/">Paper</a></td> <td><a href="https://github.com/zhysora/UCGAN">Code</a></td> <td>TGRS</td> <td>GAN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>D2TNet</td> <td>A ConvLSTM Network with Dual-direction Transfer for Pan-sharpening</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9761261">Paper</a></td> <td><a href="https://github.com/Meiqi-Gong/D2TNet">Code</a></td> <td>TGRS</td> <td>CNN</td> <td>有监督</td> <td>2022</td> </tr> <tr> <td>P2Sharpen</td> <td>P2Sharpen: A progressive pansharpening network with deep spectral transformation</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253522001798">Paper</a></td> <td><a href="https://github.com/Baixuzx7/P2Sharpen">Code</a></td> <td>INFFus</td> <td>CNN</td> <td>有监督</td> <td>2023</td> </tr> </tbody> </table>通用图像融合框架(General Image Fusion Framerwork)
<table> <thead> <tr> <th>方法</th> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>基础框架</th> <th>监督范式</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>IFCNN</td> <td>IFCNN: A general image fusion framework based on convolutional neural network</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253518305505?casa_token=aECX-sSMXqUAAAAA:xdsAZ2lJjOrUmIWTEvDO1mdCzsA9eaDxgkxiL1r4RiX_DP21TVxAiy2jkMOsFXyzVQZ4gp3NEAPj">Paper</a></td> <td><a href="https://github.com/uzeful/IFCNN">Code</a></td> <td>InFus</td> <td>CNN</td> <td>有监督</td> <td>2020</td> </tr> <tr> <td>FusionDN</td> <td>FusionDN: A Unified Densely Connected Network for Image Fusion </td> <td><a href="https://ojs.aaai.org/index.php/AAAI/article/view/6936">Paper</a></td> <td><a href="https://github.com/hanna-xu/FusionDN">Code</a></td> <td>AAAI</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>PMGI</td> <td>Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity </td> <td><a href="https://ojs.aaai.org/index.php/AAAI/article/view/6975">Paper</a></td> <td><a href="https://github.com/HaoZhang1018/PMGI_AAAI2020">Code</a></td> <td>AAAI</td> <td>CNN</td> <td>无监督</td> <td>2020</td> </tr> <tr> <td>CU-Net</td> <td>Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9055063/">Paper</a></td> <td><a href="https://github.com/cindydeng1991/TPAMI-CU-Net">Code</a></td> <td>TPAMI</td> <td>CNN</td> <td>有监督</td> <td>2021</td> </tr> <tr> <td>SDNet</td> <td>SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion</td> <td><a href="https://link.springer.com/article/10.1007/s11263-021-01501-8">Paper</a></td> <td><a href="https://github.com/HaoZhang1018/SDNet">Code</a></td> <td>IJCV</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>DIF-Net</td> <td>Unsupervised Deep Image Fusion With Structure Tensor Representations</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/8962327/">Paper</a></td> <td><a href="https://github.com/kimphys/DIFNet.pytorch">Code</a></td> <td>TIP</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>IFSepR</td> <td>IFSepR: A general framework for image fusion based on separate representation learning</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9622164">Paper</a></td> <td></td> <td>TMM</td> <td>AE</td> <td>自监督</td> <td>2021</td> </tr> <tr> <td>MTOE</td> <td>Multiple Task-Oriented Encoders for Unified Image Fusion</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9428212">Paper</a></td> <td></td> <td>ICME</td> <td>CNN</td> <td>无监督</td> <td>2021</td> </tr> <tr> <td>U2Fusion</td> <td>U2Fusion: A Unified Unsupervised Image Fusion Network</td> <td><a href="https://ieeexplore.ieee.org/abstract/document/9151265">Paper</a></td> <td><a href="https://github.com/hanna-xu/U2Fusion">Code</a></td> <td>TPAMI</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>SwinFusion</td> <td>SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer</td> <td><a href="https://ieeexplore.ieee.org/document/9812535">Paper</a></td> <td><a href="https://github.com/Linfeng-Tang/SwinFusion">Code</a></td> <td>JAS</td> <td>Transformer</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>DeFusion</td> <td>Fusion from Decomposition: A Self-Supervised Decomposition Approach for Image Fusion</td> <td><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780706.pdf">Paper</a></td> <td><a href="https://github.com/erfect2020/DecompositionForFusion">Code</a></td> <td>ECCV</td> <td>CNN</td> <td>无监督</td> <td>2022</td> </tr> <tr> <td>UIFGAN</td> <td>UIFGAN: An unsupervised continual-learning generative adversarial network for unified image fusion</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253522000756">Paper</a></td> <td><a href="">Code</a></td> <td>INFFus</td> <td>GAN</td> <td>无监督</td> <td>2023</td> </tr> </tbody> </table>综述(Survey)
<table> <thead> <tr> <th>标题</th> <th>论文</th> <th>代码</th> <th>发表期刊或会议</th> <th>年份</th> </tr> </thead> <tbody> <tr> <td>A review of remote sensing image fusion methods</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253516300173?casa_token=1JHuWMUO20QAAAAA:gzRSAmDvI5iciNDoihkI8raYANyPb6PzXE-f9U5iE9LUh9FIsfOiMBLK4VFyA6l84eDIV9HOIKs6">Paper</a></td> <td></td> <td>InFus</td> <td>2016</td> </tr> <tr> <td>Pixel-level image fusion: A survey of the state of the art</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253516300458?casa_token=8TemDHLkC_oAAAAA:ucXl6DUaMy9PpEnRueneNDpDBMeDy8P5M6JAaLkhg1Y6dvA9fdFHnuq9aXMapfMgfC18Ffa5eT07">Paper</a></td> <td></td> <td>InFus</td> <td>2017</td> </tr> <tr> <td>Deep learning for pixel-level image fusion: Recent advances and future prospects</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253517305936?casa_token=PPYorDBH2wAAAAAA:6FssewzwcKsdfVDQGoQH9Uu154HtjOTw05vXK2S7c3g02QzRLroC1gJ7MXwdMCwqsC2Mx9UWgEx5">Paper</a></td> <td></td> <td>InFus</td> <td>2018</td> </tr> <tr> <td>Infrared and visible image fusion methods and applications: A survey</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253517307972?casa_token=HpeaaU2oKNIAAAAA:MgWpCued5JhnK3KDBLQ9dbpzSORbThpSfTPXFGJDqYEMCl6C1nmTI63MG_FzaDBnGPmJKUxW-KAt">Paper</a></td> <td></td> <td>InFus</td> <td>2019</td> </tr> <tr> <td>Multi-focu image fusion: A Survey of the state of the art</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253520303109?casa_token=yugI5Tt6HAMAAAAA:jT4uV1xK2HjtfZWPuh2FkcqtHC1CN5TAYGR64ZWika5B0xAro9r-S5qOk30UDrNziKDuWPLQIsWw">Paper</a></td> <td></td> <td>InFus</td> <td>2020</td> </tr> <tr> <td>Image fusion meets deep learning: A survey and perspective</td> <td><a href="https://www.sciencedirect.com/science/article/pii/S1566253521001342?casa_token=4BDX2mCr4VwAAAAA:LzXkYoP1QwTW5lXEpMCdVEZ2W27ZXf8VbJIVOX10dl-SpYuuYkNyaTk7uiD5JCjSbn6grf8DXy33">Paper</a></td> <td></td> <td>InFus</td> <td>2021</td> </tr> <tr> <td>Deep Learning-based Multi-focus Image Fusion: A Survey and A Comparative Study </td> <td><a href="https://ieeexplore.ieee.org/document/9428544/">Paper</a></td> <td><a href="https://github.com/xingchenzhang/MFIFB">Code</a></td> <td>TPAMI </td> <td>2021</td> </tr> <tr> <td>Benchmarking and comparing multi-exposure image fusion algorithms</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253521000233?casa_token=3fZWWc9-l_kAAAAA:en9qngjXa_neaqkNdp73a25flyglW2IzNkjuYSqUYh4ZxvZy63n48-pzPboX1p2I95ofJxiz2ptp">Paper</a></td> <td><a href="https://github.com/xingchenzhang/MEFB">Code</a></td> <td>InFus</td> <td>2021</td> </tr> <tr> <td>Current advances and future perspectives of image fusion: A comprehensive review</td> <td><a href="https://www.sciencedirect.com/science/article/abs/pii/S1566253522001518">Paper</a></td> <td><a href="">Code</a></td> <td>InFus</td> <td>2023</td> </tr> </tbody> </table>数据集(Dataset)
<table> <thead> <tr> <th>融合场景</th> <th>数据集</th> <th>下载链接</th> </tr> </thead> <tbody> <tr> <td rowspan="6">红外和可见光图像融合</td> <td>TNO</td> <td><a href="https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029">https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029</a></td> </tr> <tr> <td>INO</td> <td><a href="https://www.ino.ca/en/technologies/video-analytics-dataset/videos/">https://www.ino.ca/en/technologies/video-analytics-dataset/videos/</a></td> </tr> <tr> <td>RoadScene</td> <td><a href="https://github.com/hanna-xu/RoadScene">https://github.com/hanna-xu/RoadScene</a></td> </tr> <tr> <td>MSRS</td> <td><a href="https://github.com/Linfeng-Tang/MSRS">https://github.com/Linfeng-Tang/MSRS</a></td> </tr> <tr> <td>LLVIP</td> <td><a href="https://bupt-ai-cz.github.io/LLVIP/">https://bupt-ai-cz.github.io/LLVIP/</a></td> </tr> <tr> <td>M3FD</td> <td><a href="https://github.com/JinyuanLiu-CV/TarDAL">https://github.com/JinyuanLiu-CV/TarDAL</a></td> </tr> <tr> <td></td> <td></td> <td></td> </tr> <tr> <td>医学图像融合</td> <td>Harvard</td> <td><a href="http://www.med.harvard.edu/AANLIB/home.html">http://www.med.harvard.edu/AANLIB/home.html</a></td> </tr> <tr> <td></td> <td></td> <td></td> </tr> <tr> <td rowspan="2">多曝光图像融合</td> <td>MEF</td> <td><a href="https://github.com/csjcai/SICE">https://github.com/csjcai/SICE</a></td> </tr> <tr> <td>MEFB</td> <td><a href="https://github.com/xingchenzhang/MEFB">https://github.com/xingchenzhang/MEFB</a></td> </tr> <tr> <td></td> <td></td> <td></td> </tr> <tr> <td rowspan="3">多聚焦图像融合</td> <td>Lytro</td> <td><a href="https://mansournejati.ece.iut.ac.ir/content/lytro-multi-focus-dataset">https://mansournejati.ece.iut.ac.ir/content/lytro-multi-focus-dataset</a></td> </tr> <tr> <td>MFI-WHU</td> <td><a href="https://github.com/HaoZhang1018/MFI-WHU">https://github.com/HaoZhang1018/MFI-WHU</a></td> </tr> <tr> <td>MFFW</td> <td><a href="https://www.semanticscholar.org/paper/MFFW%3A-A-new-dataset-for-multi-focus-image-fusion-Xu-Wei/4c0658f338849284ee4251a69b3c323908e62b45">https://www.semanticscholar.org/paper/MFFW%3A-A-new-dataset-for-multi-focus-image-fusion-Xu-Wei/4c0658f338849284ee4251a69b3c323908e62b45</a></td> </tr> <tr> <td></td> <td></td> <td></td> </tr> <tr> <td rowspan="4">全色图像锐化</td> <td>GaoFen</td> <td><a href="https://directory.eoportal.org/web/eoportal/satellite-missions/g">https://directory.eoportal.org/web/eoportal/satellite-missions/g</a></td> </tr> <tr> <td>WorldView</td> <td><a href="https://worldview.earthdata.nasa.gov/">https://worldview.earthdata.nasa.gov/</a></td> </tr> <tr> <td>GeoEye</td> <td><a href="https://earth.esa.int/eogateway/missions/geoeye-1">https://earth.esa.int/eogateway/missions/geoeye-1</a></td> </tr> <tr> <td>QuickBird</td> <td><a href="https://www.satimagingcorp.com/satellite-sensors/quickbird/">https://www.satimagingcorp.com/satellite-sensors/quickbird/</a></td> </tr> </tbody> </table>评估指标(Evaluation Metric)
通用评估指标(General evaluation metric)
通用评估指标位于：https://github.com/Linfeng-Tang/Image-Fusion/tree/main/General%20Evaluation%20Metric or https://github.com/Linfeng-Tang/Evaluation-for-Image-Fusion
遥感影像融合评估指标(Evaluation metric for pansharpening)
Citation
如果我们的总结对你有所帮助, 请引用以下论文：
@article{Tang2022Survey,
title={Deep learning-based image fusion: A survey},
author={Tang, Linfeng and Zhang, Hao and Xu, Han and Ma, Jiayi},
journal={Journal of Image and Graphics}
}
@article{Ma2022SwinFusion,
title={SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer},
author={Ma, Jiayi and Tang, Linfeng and Fan, Fan and Huang, Jun and Mei, Xiaoguang and Ma, Yong},
journal={IEEE/CAA Journal of Automatica Sinica},
volume={9},
number={7},
pages={1200--1217},
year={2022},
publisher={IEEE}
}
@article{TANG2022SeAFusion,
title = {Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network},
author = {Linfeng Tang and Jiteng Yuan and Jiayi Ma},
journal = {Information Fusion},
volume = {82},
pages = {28-42},
year = {2022},
issn = {1566-2535},
publisher={Elsevier}
}
@article{Tang2022PIAFusion,
title={PIAFusion: A progressive infrared and visible image fusion network based on illumination aware},
author={Tang, Linfeng and Yuan, Jiteng and Zhang, Hao and Jiang, Xingyu and Ma, Jiayi},
journal={Information Fusion},
volume = {83-84},
pages = {79-92},
year = {2022},
issn = {1566-2535},
publisher={Elsevier}
}
@article{Tang2022DIVFusion,
title={DIVFusion: Darkness-free infrared and visible image fusion},
author={Tang, Linfeng and Xiang, Xinyu and Zhang, Hao and Gong, Meiqi and Ma, Jiayi},
journal={Information Fusion},
volume = {91},
pages = {477-493},
year = {2023},
publisher={Elsevier}
}
@article{ma2021STDFusionNet,
title={STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection},
author={Jiayi Ma, Linfeng Tang, Meilong Xu, Hao Zhang, and Guobao Xiao},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2021},
volume={70},
number={},
pages={1-13},
doi={10.1109/TIM.2021.3075747}，
publisher={IEEE}
}
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