---
title: ZeroIG Low-Light Enhancement
emoji: 🌟
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
---

# ZeroIG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement

🎉 **CVPR 2024** | Zero-shot low-light image enhancement without training data

## 🚀 Quick Start

Upload a low-light image and get an enhanced version in seconds! No training required.

## 📖 About

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.

### ✨ Key Features

- **Zero-shot**: No training data required
- **Joint processing**: Simultaneous denoising and enhancement
- **Illumination-guided**: Smart adaptive enhancement
- **Prevents artifacts**: Avoids over-enhancement and localized overexposure
- **Real-time**: Fast processing for practical use

### 🔬 How it Works

1. **Illumination Estimation**: Extracts near-authentic illumination from the input
2. **Adaptive Enhancement**: Applies different enhancement levels based on pixel intensity  
3. **Joint Denoising**: Removes noise while preserving image details
4. **Artifact Prevention**: Prevents common enhancement artifacts

## 📊 Performance

ZeroIG outperforms state-of-the-art methods on standard benchmarks while requiring no training data.

## 🎯 Use Cases

- **Photography**: Rescue underexposed photos
- **Security**: Enhance surveillance footage  
- **Mobile**: Real-time camera enhancement
- **Medical**: Improve low-light medical imaging
- **Astronomy**: Enhance night sky photography

## 🖼️ Supported Formats

- JPEG, PNG, TIFF, BMP
- RGB color images
- Various resolutions (optimized for typical photo sizes)

## ⚡ Tips for Best Results

- Works best with real low-light photos (not artificially darkened)
- Indoor and outdoor scenes both supported
- Processing time varies with image size (typically 10-30 seconds)

## 📚 Citation

If you use this work, please cite:

```bibtex
@inproceedings{shi2024zero,
    title={ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light Images},
    author={Shi, Yiqi and Liu, Duo and Zhang, Liguo and Tian, Ye and Xia, Xuezhi and Fu, Xiaojing},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={3015--3024},
    year={2024}
}
```

## 🔗 Links

- 📄 [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)
- 💻 [Code](https://github.com/Doyle59217/ZeroIG)
- 📊 [Supplement](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_ZERO-IG_Zero-Shot_Illumination-Guided_CVPR_2024_supplemental.pdf)

## 🛠️ Technical Details

- **Framework**: PyTorch
- **CUDA**: Supported for GPU acceleration
- **Memory**: Optimized for various image sizes
- **Dependencies**: See requirements.txt

## 👥 Authors

Yiqi Shi, Duo Liu, Liguo Zhang, Ye Tian, Xuezhi Xia, Xiaojing Fu

## 📄 License

MIT License - see LICENSE file for details

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