基于深度学习的红外和可见光图像融合论文及代码整理

基于深度学习的红外和可见光图像融合论文及代码整理

首先附上近期整理基于深度学习的图像融合论文的思维导图

本篇博客主要整理基于深度学习的红外和可见光图像融合的论文和代码
图像融合系列博客还有：

1. 图像融合论文及代码整理最全大合集参见：图像融合论文及代码整理最全大合集
2. 图像融合综述论文整理参见：图像融合综述论文整理
3. 图像融合评估指标参见：红外和可见光图像融合评估指标
4. 图像融合常用数据集整理参见：图像融合常用数据集整理
5. 通用图像融合框架论文及代码整理参见：通用图像融合框架论文及代码整理
6. 基于深度学习的红外和可见光图像融合论文及代码整理参见:基于深度学习的红外和可见光图像融合论文及代码整理
7. 更加详细的红外和可见光图像融合代码参见：红外和可见光图像融合论文及代码整理
8. 基于深度学习的多曝光图像融合论文及代码整理参见：基于深度学习的多曝光图像融合论文及代码整理
9. 基于深度学习的多聚焦图像融合论文及代码整理参见：基于深度学习的多聚焦图像融合(Multi-focus Image Fusion)论文及代码整理
10. 基于深度学习的全色图像锐化论文及代码整理参见：基于深度学习的全色图像锐化(Pansharpening)论文及代码整理
11. 基于深度学习的医学图像融合论文及代码整理参见：基于深度学习的医学图像融合(Medical image fusion)论文及代码整理
12. 彩色图像融合参见: 彩色图像融合
13. SeAFusion:首个结合高级视觉任务的图像融合框架参见：SeAFusion:首个结合高级视觉任务的图像融合框架

基于自编码器的图像融合框架

1. DenseFuse: A Fusion Approach to Infrared and Visible Images [DenseFuse(TIP 2019)] [Paper] [Code]

2. NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models [NestFuse(TIM 2020)] [Paper] [Code]

3. RFN-Nest: An end-to-end residual fusion network for infrared and visible images [RFN-Nest (IF 2021)] [Paper] [Code]

4. Classification Saliency-Based Rule for Visible and Infrared Image Fusion [CSF (TCI 2021)] [Paper] [Code]

5. DRF: Disentangled Representation for Visible and Infrared Image Fusion [DRF(TIM 2021)] [Paper] [Code]

6. SEDRFuse: A Symmetric Encoder–Decoder With Residual Block Network for Infrared and Visible Image Fusion [SEDRFuse (TIM 2021)] [Paper] [Code]

7. Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion [EAGIF (TCSVT 2021)] [Paper]

基于卷积神经网络的图像融合框架

1. A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion [D2LE (TIP 2019)] [Paper]

2. Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion [Meta Learning(TIP 2021)] [Paper]

3. Searching a Hierarchically Aggregated Fusion Architecture for Fast Multi-Modality Image Fusion [HAF(ACM MM 2021)] [Paper] [Code]

4. RXDNFuse: A aggregated residual dense network for infrared and visible image fusion [RXDNFuse(IF 2021)] [Paper]

5. STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection [STDFusionNet(TIM 2021)] [Paper] [Code]

6. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network [SeAFusion(IF 2022)] [Paper] [Code]

7. PIAFusion: A progressive infrared and visible image fusion network based on illumination aware [PIAFusion(IF 2022)] [Paper] [Code]

基于生成对抗网络的图像融合框架

1. FusionGAN: A generative adversarial network for infrared and visible image fusion [FusionGAN(IF 2019)] [Paper] [Code]

2. Infrared and visible image fusion via detail preserving adversarial learning [Detail-GAN(IF 2021)] [Paper] [Code]

3. Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators. [DDcGAN (IJCAI 2019)] [Paper] [Code]

4. Image fusion based on generative adversarial network consistent with perception [DDcGAN(TIP 2020)] [Paper] [Code]

5. GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion [GANMcC(TIM 2020)] [Paper] [Code]

6. Image fusion based on generative adversarial network consistent with perception [Perception-GAN(IF 2021))] [Paper] [Code]

7. Semantic-supervised Infrared and Visible Image Fusion via a Dual-discriminator Generative Adversarial Network [SDDGAN(TMM 2021)] [Paper] [Code]

8. AttentionFGAN: Infrared and Visible Image Fusion Using Attention-Based Generative Adversarial Networks [AttentionFGAN(TMM 2021)] [Paper]

9. GAN-FM: Infrared and Visible Image Fusion Using GAN With Full-Scale Skip Connection and Dual Markovian Discriminators [GAN-FM(TCI 2021)] [Paper] [Code]

10. Multigrained Attention Network for Infrared and Visible Image Fusion [GAN-FM(TCI 2021)] [Paper]

11. Infrared and Visible Image Fusion via Texture Conditional Generative Adversarial Network [TC-GAN(TCSVT 2021)] [Paper] [Code]