基于深度学习的医学图像融合(Medical image fusion)论文及代码整理

基于深度学习的医学图像融合(Medical image fusion)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医学图像融合（Medical image fusion）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. 彩色图像融合参见: 彩色图像融合
12. 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

基于卷积神经网络的医学图像融合框架

1. A medical image fusion method based on convolutional neural networks [CNN(ICIF 2017)] [Paper]

2. Zero-Learning Fast Medical Image Fusion [Zero-LMF(ICIF 2019)] [Paper] [Code]

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

4. EMFusion: An unsupervised enhanced medical image fusion network [EMFusion(IF 2021)] [Paper] [Code]

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

6. Green Fluorescent Protein and Phase Contrast Image Fusion Via Detail Preserving Cross Network [DPCN-Fusion(TCI 2021)] [Paper] [Code]

7. A multiscale residual pyramid attention network for medical image fusion [MSRPAN(BSPC 2021)] [Paper] [Code]

基于生成对抗网络的医学图像融合框架

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

2. MGMDcGAN: Medical Image Fusion Using Multi-Generator Multi-Discriminator Conditional Generative Adversarial Network [MGMDcGAN(Access 2020)] [Paper] [Code]

3. Medical image fusion method based on dense block and deep convolutional generative adversarial network [DCGAN(NCAA 2021)] [Paper]

4. Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative Adversarial Networks [GFPPC-GAN(CMMM 2019)] [Paper]