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rajtiwariee/
lumitrace

Name: lumitrace
Availability: InStock

pytorch

low-light-enhancement

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LumiTrace: Temporal Low-Light Video Enhancement

LumiTrace is a state-of-the-art temporal video enhancement model designed to brighten low-light videos while maintaining temporal consistency and preserving fine details.

Model Description

LumiTrace combines the power of RetinexFormer (a Retinex-based image enhancement architecture) with custom temporal modules to process video sequences. It uses a 2-stage training strategy to achieve exceptional performance on challenging low-light scenarios.

Key Features

🎬 Temporal Consistency: Processes 3-frame sequences to eliminate flickering
🌟 High Quality: Achieves 22+ dB PSNR and 0.83+ SSIM on LOL benchmarks
⚡ Memory Efficient: Supports high-resolution inference via tiled processing
🔧 Production Ready: Includes video processing pipeline with automatic resolution standardization

Architecture

Base: RetinexFormer (2.2M parameters)
Temporal Modules: Custom 3D convolution + attention (0.8M parameters)
Total Parameters: ~3M
Input: 3-frame sequences (RGB, [0,1] normalized)
Output: Enhanced center frame

Training Data

The model was trained on:

LOL-v1: 485 training pairs, 15 test pairs
LOL-v2-Real: 689 training pairs, 100 test pairs

Training used synthetic temporal sequences generated from static image pairs with brightness/spatial augmentation.

Training Procedure

Two-Stage Training Strategy

Stage 1 (50 epochs):

Freeze RetinexFormer backbone
Train only temporal modules
Learning rate: 1e-4
Loss: L2 reconstruction + temporal consistency

Stage 2 (60 epochs):

Unfreeze all parameters
Discriminative learning rates:
- RetinexFormer: 1e-6
- Temporal modules: 1e-4
Loss: L2 + Temporal + Perceptual (VGG)

Performance

Dataset	PSNR	SSIM
LOL-v1	22.70 dB	0.8389
LOL-v2-Real	21.72 dB	0.8199

Visual Results

Sample Comparisons

Side-by-side comparisons showing input (low-light) on the left and LumiTrace enhanced output on the right:

Video Demonstration

Input (Low-Light):

Enhanced Output:

GIFs demonstrate temporal consistency and flickering reduction. Full videos available in the videos folder.

Usage

Installation

bash

git clone https://github.com/yourusername/LumiTrace
cd LumiTrace
pip install -r requirements.txt

Inference (Python)

python

from lumitrace.inference import VideoEnhancer
import yaml

# Load config
with open('configs/lol_v1_temporal.yml', 'r') as f:
    config = yaml.safe_load(f)

# Initialize enhancer
enhancer = VideoEnhancer(
    model_path='checkpoints/lol_v1/stage2/best.pth',
    config=config
)

# Process video
enhancer.enhance_video(
    input_path='input.mp4',
    output_path='enhanced.mp4'
)

Inference (CLI)

./scripts/enhance_video.sh input.mp4 output.mp4

Limitations

Trained primarily on indoor/static scenes (LOL dataset)
May struggle with extreme motion or outdoor dynamic lighting
Best performance on videos with resolution ≤720p
Requires GPU for real-time processing

Citation

If you use this model, please cite:

bibtex

@software{lumitrace2024,
  title={LumiTrace: Temporal Low-Light Video Enhancement},
  author={Your Name},
  year={2024},
  url={https://github.com/yourusername/LumiTrace}
}

Acknowledgments

Based on RetinexFormer
Trained on LOL datasets

License

Apache 2.0

rajtiwariee/lumitrace