@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
📖[论文下载地址]
💽[代码下载地址]
📖论文解读
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
🔑关键词
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
💭核心思想
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……
参考链接
[什么是图像融合？（一看就通，通俗易懂）]
🪢网络结构
作者提出的网络结构如下所示。
x
📉损失函数
🔢数据集
- Train：KAIST
- TNO, VOT2020-RGBT
图像融合数据集链接
[图像融合常用数据集整理]
🎢训练设置
🔬实验
📏评价指标
- EN
- SD
- SSIMm
- MI
- VIFm
- Nabf
参考资料
[图像融合定量指标分析]
🥅Baseline
- DenseFuse, FusionGAN, IFCNN, CUNet, RFN-Nest, Tes2Fusion, YDTR, SwinFusion, U2Fusion
✨✨✨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]✨✨✨
🔬实验结果
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]
🚀传送门
📑图像融合相关论文阅读笔记
📑[(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]
📚图像融合论文baseline总结
📚[图像融合论文baseline及其网络模型]
📑其他论文
📑[3D目标检测综述：Multi-Modal 3D Object Detection in Autonomous Driving：A Survey]
🎈其他总结
🎈[CVPR2023、ICCV2023论文题目汇总及词频统计]
✨精品文章总结
✨[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]文章来源：https://www.toymoban.com/news/detail-813050.html
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