@ARTICLE{10105495,
author={Li, Hui and Xu, Tianyang and Wu, Xiao-Jun and Lu, Jiwen and Kittler, Josef},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images},
year={2023},
volume={45},
number={9},
pages={11040-11052},
doi={10.1109/TPAMI.2023.3268209}}
论文级别:SCI A1
影响因子:23.6
作者构建了一种【端到端】的【轻量级】融合网络,该模型使用训练测试策略避免了网络设计步骤。具体来说,对融合任务使用了【可学习的表达方法】,其网络模型构建是由生成可学习模型的优化算法指导的。【低秩表达】(low-rank representation ,【LRR】)是算法核心基础。
并提出了一种新的细节语义信息损失函数
image fusion, network architecture, optimal model, infrared image, visible image.
图像融合,网络结构,优化模型,红外图像,可见光图像
看的不是很懂,感觉和CDDFuse有点像,都是分别从源图像提取两个不同的特征,然后将不同源图像相同的特征拼接在一起,然后融合,然后重构生成融合图像。本文最大的创新应该就是LLRR-Blocks,使用这个东西可以避免设计复杂的网络结构,作者把问题公式化了。(我理解的很浅)
回头再看看吧
待更新……
作者提出的网络结构如下所示。
x
图像融合数据集链接
[图像融合常用数据集整理]
参考资料
[图像融合定量指标分析]
???参考资料
???强烈推荐必看博客[图像融合论文baseline及其网络模型]???
更多实验结果及分析可以查看原文:
📖[论文下载地址]
💽[代码下载地址]
📑[(DeFusion)Fusion from decomposition: A self-supervised decomposition approach for image fusion]
📑[ReCoNet: Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion]
📑[RFN-Nest: An end-to-end resid- ual fusion network for infrared and visible images]
📑[SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images]
📑[SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer]
📑[(MFEIF)Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion]
📑[DenseFuse: A fusion approach to infrared and visible images]
📑[DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pair]
📑[GANMcC: A Generative Adversarial Network With Multiclassification Constraints for IVIF]
📑[DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion]
📑[IFCNN: A general image fusion framework based on convolutional neural network]
📑[(PMGI) Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity]
📑[SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion]
📑[DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion]
📑[FusionGAN: A generative adversarial network for infrared and visible image fusion]
📑[PIAFusion: A progressive infrared and visible image fusion network based on illumination aw]
📑[CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion]
📑[U2Fusion: A Unified Unsupervised Image Fusion Network]
📑综述[Visible and Infrared Image Fusion Using Deep Learning]
📑[3D目标检测综述:Multi-Modal 3D Object Detection in Autonomous Driving:A Survey]
🎈[CVPR2023、ICCV2023论文题目汇总及词频统计]
?[图像融合论文及代码整理最全大合集]
?[图像融合常用数据集整理]
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