PromptFusion

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[JAS 2024] Official implementation for "PromptFusion: Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion".

PromptFusion 최신버젼 다운로드

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# PromptFusion: Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion

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### PromptFusion: Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion [IEEE/CAA JAS 2024]

By Jinyuan Liu, Xingyuan Li, Zirui Wang, Zhiying Jiang, Wei Zhong, Wei Fan and Bin Xu

## Updates
[2025-2-18] We released checkpoints and codes of our method, you can test or train our method according to the instructions.

[2024-12-05] Our paper is available online! [[Paper](https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2024.124878)]

## Requirements
- CUDA 12.0
- Python 3.8.10
- Pytorch 2.1.1
## Training
1. Environment Preparation
```
# create virtual environment
conda create -n promptfusion python=3.8.10
conda activate promptfusion
# install requirements
pip install -r requirements.txt
```
2. Data Preparation
   
We use [MSRS](https://github.com/Linfeng-Tang/MSRS) dataset for training, and [M3FD](https://github.com/JinyuanLiu-CV/TarDAL), [TNO](http://figshare.com/articles/TNO\_Image\_Fusion\_Dataset/1008029), [RoadScene](https://github.com/hanna-xu/RoadScene) datasets for evalution.
   
Run
```
python dataprocessing.py
```
to process the data before training.

3. Start training
   
Run the training code to get started.
```
python train.py
```
## Testing
Our checkpoints can be found in [Google drive](https://drive.google.com/file/d/113WT_6Jz3sCx2x7qmaGBHftXGfzoQewg/view?usp=drive_link), put it in 'PromptFusion/models/', you can test our method through
```
python test_IVF.py
```
## Experiments results

## Citation
```
@article{liu2024promptfusion,
  title={PromptFusion: Harmonized semantic prompt learning for infrared and visible image fusion},
  author={Liu, Jinyuan and Li, Xingyuan and Wang, Zirui and Jiang, Zhiying and Zhong, Wei and Fan, Wei and Xu, Bin},
  journal={IEEE/CAA Journal of Automatica Sinica},
  year={2024},
  publisher={IEEE}
}
```
## Acknowledgement
Our codes are based on [CDDFuse](https://github.com/Zhaozixiang1228/MMIF-CDDFuse), [Contourlet-CNN](https://github.com/xKHUNx/Contourlet-CNN), thanks for their contribution.

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