README.md

metadata

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:

@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
• 💻 Code
• 📊 Supplement

🛠️ 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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