图像融合论文阅读：LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Imag

@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}}

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论文级别：SCI A1
影响因子：23.6

[论文下载地址]
[代码下载地址]

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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]

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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]

如有疑问可联系：[email protected];
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