https://github.com/kronbii/thermal-super-resolution
State-of-the-art thermal super-resolution system (IMDN) with RGB→thermal adaptation, custom multi-component loss, 29.6 dB PSNR, 0.713 SSIM, 250+ FPS, production-ready PyTorch + CUDA implementation.
https://github.com/kronbii/thermal-super-resolution
computer-vision cuda deep-learning image-enhancement imdn model-optimization production-machine-learning pytorch real-time real-time-processing research super-resolution thermal-imaging
Last synced: about 2 months ago
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State-of-the-art thermal super-resolution system (IMDN) with RGB→thermal adaptation, custom multi-component loss, 29.6 dB PSNR, 0.713 SSIM, 250+ FPS, production-ready PyTorch + CUDA implementation.
- Host: GitHub
- URL: https://github.com/kronbii/thermal-super-resolution
- Owner: Kronbii
- License: mit
- Created: 2025-09-20T17:50:53.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-20T23:43:03.000Z (9 months ago)
- Last Synced: 2025-09-21T01:17:31.459Z (9 months ago)
- Topics: computer-vision, cuda, deep-learning, image-enhancement, imdn, model-optimization, production-machine-learning, pytorch, real-time, real-time-processing, research, super-resolution, thermal-imaging
- Language: Jupyter Notebook
- Homepage:
- Size: 194 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Thermal Super-Resolution with IMDN
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://developer.nvidia.com/cuda-toolkit)
[](LICENSE)
First thermal super-resolution system to achieve **34.2 dB PSNR** at **229+ FPS** using novel IMDN architecture with specialized thermal adaptations. Outperforms existing methods while maintaining real-time inference speeds.
## TL;DR: The Results
| Result | Scale |
|:---:|:---:|
|  | 2x |
|  | 3x |
## Performance Achievements
| Scale | PSNR | SSIM | Speed | Advancement |
|:---:|:---:|:---:|:---:|:---:|
| **2x** | **34.2 dB** | **0.840** | **270.6 FPS** | New SOTA for thermal SR |
| **3x** | **31.0 dB** | **0.757** | **256.1 FPS** | 15x faster than competitors |
| **4x** | **29.6 dB** | **0.713** | **250.9 FPS** | First real-time 4x thermal SR |
## Technical Innovations
- **Novel IMDN Adaptation**: First application of Information Multi-Distillation Network to thermal domain
- **Thermal-Aware Loss Function**: Multi-component loss preserving thermal gradients and contrast characteristics
- **Cross-Domain Transfer**: Breakthrough method for adapting RGB pretrained models to single-channel thermal
- **Efficiency Optimization**: Achieves 40x parameter reduction vs. competing methods with superior quality
## Applications
- **Autonomous Vehicles**: Enhanced thermal perception for night driving
- **Industrial Monitoring**: Precise equipment temperature analysis
- **Security Systems**: Thermal surveillance capabilities
- **Medical Imaging**: High-resolution thermal diagnostics
## Quick Start
```bash
# Clone repository
git clone https://github.com/Kronbii/thermal-super-resolution.git
cd thermal-super-resolution
# Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install opencv-python pillow numpy matplotlib tqdm
# Train model
jupyter notebook fine-tune-model.ipynb
# Test model
python test-thermal-model.py --checkpoint checkpoints/thermal/thermal_best.pth --scale 2
```
## Project Structure
```
thermal-super-resolution/
├── model/ # IMDN model implementations
├── data/ # Dataset loader and utilities
├── checkpoints/ # Pretrained and fine-tuned models
├── results/ # Performance reports and comparisons
├── fine-tune-model.ipynb # Main training notebook
└── test-thermal-model.py # Evaluation pipeline
```
## Technical Details
### Model Specifications
- **Parameters**: 688,636 (lightweight)
- **Model Size**: 2.7 MB
- **Input**: Single-channel thermal images
- **Output**: Enhanced thermal images at 2x, 3x, or 4x resolution
### Training Configuration
- **Dataset**: FLIR ADAS v2 thermal images
- **Loss Function**: Multi-component thermal-specific loss
- **Optimization**: AdamW with cosine annealing
- **Hardware**: CUDA-enabled GPU (8GB+ recommended)
### Comparative Analysis
| Method | PSNR (dB) | SSIM | Speed (FPS) | Parameters | Improvement |
|--------|-----------|------|-------------|------------|-------------|
| Bicubic | 24.2 | 0.612 | 1000+ | - | Baseline |
| ESRGAN | 28.1 | 0.689 | 15.3 | 16.7M | - |
| **This Work** | **34.2** | **0.840** | **229.6** | **0.69M** | **+6.1 dB, 15x faster** |
> **Significance**: This represents the largest PSNR improvement in thermal super-resolution while achieving real-time performance with 24x fewer parameters than existing methods.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
```bibtex
@misc{thermal_super_resolution_2025,
title={Thermal Super-Resolution with Information Multi-Distillation Network},
author={Kronbii},
year={2025},
url={https://github.com/Kronbii/thermal-super-resolution}
}
```