@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]
文章目录
论文解读关键词核心思想甆网络结构损失函数数据集训练设置实验评价指标聾Baseline实验结果
传送门图像融合相关论文阅读笔记图像融合论文baseline总结其他论文其他总结✨精品文章总结
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）]
甆网络结构
作者提出的网络结构如下所示。 x
损失函数
数据集
Train：KAISTTNO, 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
ENSDSSIMmMIVIFmNabf
参考资料 [图像融合定量指标分析]
聾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]
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