# AGMFusion
**Repository Path**: qazYP/AGMFusion
## Basic Information
- **Project Name**: AGMFusion
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-25
- **Last Updated**: 2025-02-25
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
π₯AGMFusion: A Real-Time End-to-End Infrared and Visible Image Fusion Network Based on Adaptive Guidance Module
1 Sun Yat-Sen University 2 Yiren Technology β Corresponding author

## π₯ Updates
- **`2024/08/13`**: β¨ We released the paper on [TechRxiv](https://www.techrxiv.org/users/813809/articles/1215064-agmfusion-a-real-time-end-to-end-infrared-and-visible-image-fusion-network-based-on-adaptive-guidance-module).
- **`2024/08/03`**: π€ We added a description of the dataset and how to make your own dataset.
- **`2024/07/21`**: π We released the initial version of the code and models. Continuous updates, stay tuned!
## Introduction π
This repo, named **AGMFusion**, contains the official PyTorch implementation of our paper [AGMFusion: A Real-Time End-to-End Infrared and Visible Image Fusion Network Based on Adaptive Guidance Module](https://ieeexplore.ieee.org/document/10605610).
We are actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) π.
If you are interested in image fusion, you can visit our [IVIF-Code-Interpretation](https://github.com/liushh39/IVIF-Code-Interpretation) to read articles with **open-source code**.
## Getting Started π
### 1. Clone the code and prepare the environment
```bash
git clone https://github.com/liushh39/AGMFusion.git
cd AGMFusion
# create env using conda
conda create -n AGMFusion python==3.7.3
conda activate AGMFusion
# install dependencies with pip
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
```
### 2. Train
Download the [dataset](https://pan.baidu.com/s/1GqxXMHKGudMJT0IsqMmQ3w?pwd=8zki), put it in `dataset`
```
βββ AGMFusion/dataset
| βββtrain
| βββ img1
| βββ img2
| βββ vi
| βββ ir
```
It does not matter if there are blank images in the img2 folder of the downloaded dataset.
Or you can create your dataset by running `python create_dataset.py`
```bash
python train.py
```
### 3. Inference π
```bash
python test.py
```
If the script runs successfully, you will get fusion results named in `fusion_results`.
Or, you can change the parameters:
```bash
# more options to see
python test.py -h
```
## Contact Informaiton
If you have any questions, please feel free to contact me at liushh39@mail2.sysu.edu.cn.
## Citation π
If you find AGMFusion useful for your research, welcome to π this repo and cite our work using the following BibTeX:
```bibtex
@ARTICLE{10605610,
author={Liu, Shenghao and Lan, Xiaoxiong and Chen, Wenyong and Zhang, Zhiyong and Qiu, Changzhen},
journal={IEEE Sensors Journal},
title={AGMFusion: A Real-Time End-to-End Infrared and Visible Image Fusion Network Based on Adaptive Guidance Module},
year={2024},
keywords={Adaptive guidance module;deep learning;image fusion;infrared and visible images},
doi={10.1109/JSEN.2024.3426274}}
```