图像融合论文阅读：Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion

@article{yue2023dif,
title={Dif-fusion: Towards high color fidelity in infrared and visible image fusion with diffusion models},
author={Yue, Jun and Fang, Leyuan and Xia, Shaobo and Deng, Yue and Ma, Jiayi},
journal={arXiv preprint arXiv:2301.08072},
year={2023}
}

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论文级别：-
影响因子：-

[论文下载地址]
[代码下载地址]

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论文解读

以往的VIF网络将多通道图像转换为单通道图像，忽略了【颜色保真】，为了解决这个问题，作者提出了【基于扩散模型】的图像融合网络【Dif-Fusion】，在具有正向扩散和反向扩散的潜在空间中，使用降噪网络【建立多通道数据分布】，然后降噪网络【提取】包含了可见光信息和红外信息的【多通道扩散特征】，最后将扩散特征输入多通道融合模块生成三通道的融合图像。

关键词

Image fusion, color fidelity, multimodal information, diffusion models, latent representation, deep generative
model.
图像融合，颜色保真度，多模态信息，扩散模型，潜在表示，深度生成模型

核心思想

将源图像通道拼接，输入扩散模型，然后从扩散模型中提取扩散特征，通过多通道的扩散特征，输入多通道融合网络中恢复出多通道的融合图像

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

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

• MI
• VIF
• SF
• Qabf
• SD

参考资料
[图像融合定量指标分析]

Baseline

• FusionGAN, SDDGAN, GANMcC, SDNet, U2Fusion, TarDAL

✨✨✨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

[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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