# MEF-GAN

**Repository Path**: wdwdwd123/MEF-GAN

## Basic Information

- **Project Name**: MEF-GAN
- **Description**: This is the code for multi-exposure image fusion via generative adversarial networks
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-08
- **Last Updated**: 2021-11-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MEF-GAN
This is the code for "multi-exposure image fusion via generative adversarial networks".

## Architecture:<br>
<div align=center><img src="https://github.com/hanna-xu/MEF-GAN/blob/master/imgs/Architecture.png" width="740" height="320"/></div><br>

## Fused results:<br>
<div align=center><img src="https://github.com/hanna-xu/MEF-GAN/blob/master/imgs/results.png" width="700" height="390"/></div>

## To train:<br>
CUDA_VISIBLE_DEVICES=0,1 python main.py <br>
(2 GPUs are needed. One is for the self-attention block and the other one is for other blocks and the discriminator.)<br>

## To test:<br>
CUDA_VISIBLE_DEVICES=0,1 python test_main.py<br>

## Tips:<br>
The training dataset is too large to be uploaded and downloaded. It may be more convenient to create your own dataset. <br>
The multi-exposure image pairs can be downloaded [*here*](https://github.com/csjcai/SICE). <br>
The code to create your own training dataset can be found [*here*](https://github.com/hanna-xu/utils).<br>
(size_input=144. The channel dimension: 1:3 over-exposed patches, 4:6 under-exposed patches, 7:9 ground-truth patches.)

If you have any question, please email to me (xu_han@whu.edu.cn).