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Code of Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning.

DDBF 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# DDBF CVPR2024
The code of Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning

````
@inproceedings{zhang2024dispel,
  title={Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning},
  author={Zhang, Hao and Tang, Linfeng and Xiang, Xinyu and Zuo, Xuhui and Ma, Jiayi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={26487--26496},
  year={2024}
}
````

#### Recommended Environment:<br>
 - [ ] python = 2.7
 - [ ] tensorflow-gpu = 1.9.0
 - [ ] numpy = 1.15.4
 - [ ] scipy = 1.2.0
 - [ ] pillow = 5.4.1
 - [ ] scikit-image = 0.13.1

#### After testing, the following configuration is also OK:<br>
 - [ ] python = 3.6
 - [ ] tensorflow-gpu = 1.9.0
 - [ ] numpy = 1.19.2
 - [ ] scipy = 1.5.4
 - [ ] pillow = 8.4.0
 - [ ] scikit-image = 0.17.2

#### Prepare data :<br>
- [ ] Put low-light images in the "Dataset/Test/Low-light/..." for testing the function of low-light enhancement
- [ ] Put multi-modal images in the "Dataset/Test/Fusion/..." for testing the complete enhancement and fusion capabilities of DDBF.

#### Testing :<br>
- [ ] Run "CUDA_VISIBLE_DEVICES=X python evaluate_Enhance.py" to enhance the provided low-light images.
- [ ] Run "CUDA_VISIBLE_DEVICES=X python evaluate_DDBF.py" to enhance and fuse the provided multi-modal images.

DDBF

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