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}
}

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论文级别：ICCV 2023
影响因子：-

[论文下载地址]
[代码下载地址]

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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]

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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：[email protected];
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