图像融合论文阅读:DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

@article{zhao2023ddfm,

title={DDFM: denoising diffusion model for multi-modality image fusion},

author={Zhao, Zixiang and Bai, Haowen and Zhu, Yuanzhi and Zhang, Jiangshe and Xu, Shuang and Zhang, Yulun and Zhang, Kai and Meng, Deyu and Timofte, Radu and Van Gool, Luc},

journal={arXiv preprint arXiv:2303.06840},

year={2023}

}


论文级别:ICCV 2023

影响因子:-

📖[论文下载地址]

💽[代码下载地址]


文章目录


📖论文解读

这篇文章和CDDFuse是同一个团队的成果。

作者利用扩散概率模型DDPM(denoising diffusion probabilistic model )用在多模态图像融合任务中,提出了去噪扩散图像融合模型(Denoising Diffusion image Fusion Model (DDFM)),融合任务被定义为了在DDPM采样网络下的条件生成问题,并进一步划分为了:无条件生成和最大似然这两个子问题。

🔑关键词

扩散概率模型,多模态图像融合

💭核心思想

以后再填坑,公式推导太多了,哭泣.gif

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。

📉损失函数

🔢数据集

  • TNO, RoadScene, MSRS, M3FD

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
  • MI
  • VIF
  • Qabf
  • SSIM

参考资料
[图像融合定量指标分析]

🥅Baseline

  • FusionGAN, GANMcC, TarDAL, UMFusion, U2Fusion, RFNet, DeFusion

✨✨✨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]


🚀传送门

📑图像融合相关论文阅读笔记

📑[Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion models]

📑[Coconet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion]

📑[LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images]

📑[(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:420269520@qq.com;

码字不易,【关注,收藏,点赞】一键三连是我持续更新的动力,祝各位早发paper,顺利毕业~

相关推荐
ZOMI酱几秒前
【AI系统】GPU 架构与 CUDA 关系
人工智能·架构
deephub7 分钟前
使用 PyTorch-BigGraph 构建和部署大规模图嵌入的完整教程
人工智能·pytorch·深度学习·图嵌入
羞儿13 分钟前
【读点论文】Text Detection Forgot About Document OCR,很实用的一个实验对比案例,将科研成果与商业产品进行碰撞
深度学习·ocr·str·std
deephub39 分钟前
优化注意力层提升 Transformer 模型效率:通过改进注意力机制降低机器学习成本
人工智能·深度学习·transformer·大语言模型·注意力机制
搏博1 小时前
神经网络问题之二:梯度爆炸(Gradient Explosion)
人工智能·深度学习·神经网络
KGback1 小时前
【论文解析】HAQ: Hardware-Aware Automated Quantization With Mixed Precision
人工智能
电子手信1 小时前
知识中台在多语言客户中的应用
大数据·人工智能·自然语言处理·数据挖掘·知识图谱
不高明的骗子1 小时前
【深度学习之一】2024最新pytorch+cuda+cudnn下载安装搭建开发环境
人工智能·pytorch·深度学习·cuda
Chef_Chen1 小时前
从0开始学习机器学习--Day33--机器学习阶段总结
人工智能·学习·机器学习
搏博1 小时前
神经网络问题之:梯度不稳定
人工智能·深度学习·神经网络