neuro-low-light 🌙

Professional low-light image enhancement using Zero-DCE++ (Zero-Reference Deep Curve Estimation).

A production-ready Python package for enhancing dark images and videos with state-of-the-art deep learning. Perfect for improving visibility in low-light conditions without requiring paired training data.

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🌟 Features

• ✅ Low-Light Enhancement - Brighten dark images naturally
• ✅ Video Enhancement - Process videos frame-by-frame
• ✅ Zero-Reference - No paired training data required
• ✅ CUDA Acceleration - GPU support for fast processing
• ✅ CPU Fallback - Works on systems without GPU
• ✅ Simple API - 3 lines of code to enhance
• ✅ Pre-trained Model - Ready to use out of the box (0.93 MB)
• ✅ Production Ready - Clean, tested, and documented

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📦 Installation

Step 1: Install Package

pip install neuro-low-light

Step 2: Verify Installation

python -c "from neuro_low_light import EnhanceModel; print('✓ Installation successful!')"

Note: Model weights (0.93 MB) are included in the package.

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Alternative: From Source

git clone https://github.com/Parshva2605/neuro-low-light.git
cd neuro-low-light
pip install -e .

Requirements

• Python >= 3.9
• PyTorch >= 2.0.0
• CUDA (optional, for GPU acceleration)

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🚀 Quick Start

Enhance an Image

from neuro_low_light import EnhanceModel

# Initialize model (auto-detects CUDA)
model = EnhanceModel(device="cuda")

# Enhance low-light image
model.enhance_image("dark_photo.jpg", "bright_photo.jpg")

Enhance a Video

from neuro_low_light import EnhanceModel

# Initialize model
model = EnhanceModel(device="cuda")

# Enhance video (with progress bar)
model.enhance_video("dark_video.mp4", "bright_video.mp4")

Process Multiple Images

from neuro_low_light import EnhanceModel

# Initialize model
model = EnhanceModel(device="cuda")

# Enhance all images in a folder
model.enhance_folder("input_folder/", "output_folder/")

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💡 Usage Examples

Command Line (Using Examples)

Image Enhancement

cd examples
python run_image.py input.jpg output.jpg

Video Enhancement

cd examples
python run_video.py input.mp4 output.mp4

Python API

Basic Usage

from neuro_low_light import EnhanceModel

# Initialize
model = EnhanceModel()

# Enhance image
model.enhance_image("low_light.jpg", "enhanced.jpg")

Custom Checkpoint

model = EnhanceModel(
    checkpoint_path="path/to/custom/model.pth",
    device="cuda"
)

Batch Processing

from pathlib import Path

model = EnhanceModel(device="cuda")

# Process all images in a directory
input_dir = Path("dark_images/")
output_dir = Path("bright_images/")

for img_path in input_dir.glob("*.jpg"):
    output_path = output_dir / img_path.name
    model.enhance_image(img_path, output_path)

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📊 Model Details

Architecture: Zero-DCE++

• Type: Deep Curve Estimation Network
• Parameters: ~80K
• Model Size: 0.93 MB
• Iterations: 8 curve adjustments
• Input: RGB images (any resolution)
• Output: Enhanced RGB images

Training Details

• Dataset: LOL (Low-Light) dataset
• Training: Zero-reference (unpaired)
• Loss: Spatial consistency + exposure control + color constancy + illumination smoothness

Performance

• Speed: ~30 FPS on RTX 3090 (1080p)
• Quality: Natural enhancement without over-saturation
• Robustness: Works on various lighting conditions

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🎯 Use Cases

• Night Photography - Enhance photos taken in dark environments
• Security Footage - Improve visibility in surveillance videos
• Underwater Images - Brighten dark underwater scenes
• Medical Imaging - Enhance low-contrast medical images
• Astrophotography - Bring out details in dark sky images
• Indoor Photography - Improve poorly lit indoor shots

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🔧 Advanced Usage

Custom Iterations

# More iterations = stronger enhancement (default: 8)
model = EnhanceModel(num_iterations=12)

Device Selection

# Use specific device
model = EnhanceModel(device="cuda:0")  # First GPU
model = EnhanceModel(device="cpu")     # CPU only
model = EnhanceModel(device="auto")    # Auto-detect (default)

Video Processing with Custom Settings

from neuro_low_light.utils import VideoReader, VideoWriter

model = EnhanceModel()

# Custom video processing with access to frames
with VideoReader("input.mp4") as reader:
    with VideoWriter("output.mp4", reader.fps, reader.width, reader.height) as writer:
        for frame in reader:
            # Process frame
            enhanced = model.enhance_frame(frame)  # Custom method
            writer.write(enhanced)

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📚 API Reference

EnhanceModel

EnhanceModel(
    checkpoint_path: Optional[str] = None,
    device: str = "auto",
    num_iterations: int = 8
)

Methods:

• enhance_image(input_path, output_path) - Enhance a single image
• enhance_video(input_path, output_path) - Enhance a video
• enhance_folder(input_folder, output_folder) - Enhance all images in a folder

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🧪 Examples

See the examples/ directory for complete examples:

• run_image.py - Image enhancement example
• run_video.py - Video enhancement example
• batch_process.py - Batch processing example

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Zero-DCE++ paper and implementation
• LOL Dataset for training data
• PyTorch team for the deep learning framework

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📧 Contact

Author: Parshva Shah
Email: shahparshva2005@gmail.com
GitHub: https://github.com/Parshva2605

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🐛 Issues & Contributions

Found a bug or want to contribute?

• Report issues: https://github.com/Parshva2605/neuro-low-light/issues
• Pull requests welcome!

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⭐ Star History

If you find this package useful, please give it a star on GitHub!

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Made with ❤️ by Parshva Shah