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--- |
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title: ZeroIG Low-Light Enhancement |
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emoji: 🌟 |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.44.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# ZeroIG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement |
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🎉 **CVPR 2024** | Zero-shot low-light image enhancement without training data |
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## 🚀 Quick Start |
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Upload a low-light image and get an enhanced version in seconds! No training required. |
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## 📖 About |
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This space implements **ZeroIG**, a novel zero-shot method for jointly denoising and enhancing low-light images. The method is completely independent of training data and noise distribution. |
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### ✨ Key Features |
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- **Zero-shot**: No training data required |
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- **Joint processing**: Simultaneous denoising and enhancement |
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- **Illumination-guided**: Smart adaptive enhancement |
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- **Prevents artifacts**: Avoids over-enhancement and localized overexposure |
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- **Real-time**: Fast processing for practical use |
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### 🔬 How it Works |
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1. **Illumination Estimation**: Extracts near-authentic illumination from the input |
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2. **Adaptive Enhancement**: Applies different enhancement levels based on pixel intensity |
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3. **Joint Denoising**: Removes noise while preserving image details |
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4. **Artifact Prevention**: Prevents common enhancement artifacts |
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## 📊 Performance |
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ZeroIG outperforms state-of-the-art methods on standard benchmarks while requiring no training data. |
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## 🎯 Use Cases |
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- **Photography**: Rescue underexposed photos |
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- **Security**: Enhance surveillance footage |
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- **Mobile**: Real-time camera enhancement |
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- **Medical**: Improve low-light medical imaging |
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- **Astronomy**: Enhance night sky photography |
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## 🖼️ Supported Formats |
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- JPEG, PNG, TIFF, BMP |
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- RGB color images |
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- Various resolutions (optimized for typical photo sizes) |
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## ⚡ Tips for Best Results |
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- Works best with real low-light photos (not artificially darkened) |
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- Indoor and outdoor scenes both supported |
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- Processing time varies with image size (typically 10-30 seconds) |
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## 📚 Citation |
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If you use this work, please cite: |
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```bibtex |
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@inproceedings{shi2024zero, |
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title={ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light Images}, |
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author={Shi, Yiqi and Liu, Duo and Zhang, Liguo and Tian, Ye and Xia, Xuezhi and Fu, Xiaojing}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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pages={3015--3024}, |
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year={2024} |
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} |
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``` |
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## 🔗 Links |
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- 📄 [Paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_ZERO-IG_Zero-Shot_Illumination-Guided_Joint_Denoising_and_Adaptive_Enhancement_for_Low-Light_CVPR_2024_paper.pdf) |
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- 💻 [Code](https://github.com/Doyle59217/ZeroIG) |
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- 📊 [Supplement](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_ZERO-IG_Zero-Shot_Illumination-Guided_CVPR_2024_supplemental.pdf) |
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## 🛠️ Technical Details |
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- **Framework**: PyTorch |
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- **CUDA**: Supported for GPU acceleration |
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- **Memory**: Optimized for various image sizes |
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- **Dependencies**: See requirements.txt |
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## 👥 Authors |
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Yiqi Shi, Duo Liu, Liguo Zhang, Ye Tian, Xuezhi Xia, Xiaojing Fu |
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## 📄 License |
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MIT License - see LICENSE file for details |
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--- |
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*Built with ❤️ using Gradio and Hugging Face Spaces* |
