---
title: Siren Super Resolution
emoji: 🔥
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 6.0.2
app_file: app.py
pinned: false
---

# 🔥 SIREN Super-Resolution Demo

A Gradio demo showcasing **SIREN** (Sinusoidal Representation Networks) for image super-resolution.

## What is SIREN?

SIREN networks use periodic activation functions (sine) instead of traditional ReLU activations, making them exceptionally well-suited for representing continuous signals and capturing fine details in images.

**Key advantages:**
- Smooth, continuous representations
- Excellent for capturing high-frequency details
- Can represent images at arbitrary resolutions
- Implicit neural representation - no upsampling layers needed!

## How This Demo Works

1. **Upload** a high-resolution image (this serves as the ground truth)
2. **Downsample** the image artificially by a selected scale factor (2x, 4x, or 8x)
3. **Train** SIREN to learn the downsampled image representation
4. **Generate** a super-resolved version at the original resolution
5. **Compare** the results: downsampled input, SIREN output, and ground truth

## Features

- 🎚️ **Multiple scale factors**: 2x, 4x, 8x super-resolution
- 📊 **Quality metrics**: PSNR, SSIM, and MAE for objective quality assessment
- 💾 **Model caching**: Save and reuse trained models to avoid retraining
- 🎨 **Improved UI**: Tabbed interface with side-by-side comparison view
- 🎛️ **Configurable model**: Adjust hidden layers, features, and training steps
- 📈 **Training visualization**: Watch the loss curve during training
- 📸 **Real sample images**: High-quality photos from Unsplash (cat, landscape, portrait, flower)

## Installation

```bash
# Install dependencies
pip install -r requirements.txt

# Generate sample images (optional - already included)
python create_samples.py

# Run the demo
python app.py
```

## Usage

### Running locally:

```bash
python app.py
```

Then open your browser to the URL shown (usually `http://127.0.0.1:7860`)

### Quick test:

```bash
python test_siren.py
```

This runs a quick test to verify the SIREN implementation works correctly.

## Files

- `app.py` - Main Gradio application
- `siren.py` - SIREN model implementation
- `utils.py` - Image processing utilities
- `create_samples.py` - Script to generate sample images
- `test_siren.py` - Quick test script
- `samples/` - Sample images for testing

## Parameters

### Model Architecture
- **Hidden Features**: Width of the network (128-512)
  - More features = more capacity but slower training
- **Hidden Layers**: Depth of the network (2-6)
  - More layers = more capacity but slower training

### Training
- **Training Steps**: Number of optimization steps (500-5000)
  - More steps = better quality but takes longer
  - 2000 steps is a good balance

### Super-Resolution
- **Scale Factor**: Downsampling/upsampling factor (2x, 4x, 8x)
  - 2x: Easier task, faster training
  - 4x: Moderate difficulty
  - 8x: Challenging, may need more steps

## Example Results

The demo shows three outputs:
1. **Downsampled (Input)**: The artificially downsampled low-resolution image
2. **Super-Resolved (SIREN)**: The SIREN-generated high-resolution output
3. **Ground Truth (Original)**: The original high-resolution image for comparison

## References

- **Paper**: [Implicit Neural Representations with Periodic Activation Functions (SIREN)](https://arxiv.org/abs/2006.09661)
- **Project Page**: [https://vsitzmann.github.io/siren/](https://vsitzmann.github.io/siren/)
- **Notebook Tutorial**: [SIREN Tutorial by Nipun Batra](https://github.com/nipunbatra/pml-teaching/blob/master/notebooks/siren.ipynb)

## Quality Metrics Explained

The demo now includes three standard image quality metrics:

- **PSNR (Peak Signal-to-Noise Ratio)**: Measures reconstruction quality in dB. Higher is better.
  - \>30 dB: Good quality
  - \>40 dB: Excellent quality

- **SSIM (Structural Similarity Index)**: Perceptual quality metric ranging from 0 to 1. Closer to 1.0 is better.
  - \>0.9: Very good quality
  - \>0.95: Excellent quality

- **MAE (Mean Absolute Error)**: Average pixel-wise difference. Lower is better.
  - <0.01: Excellent
  - <0.05: Good

## Model Caching

Trained models are automatically saved and can be reused:

- Models are cached in `model_cache/` directory
- Cache key includes: image size, scale factor, training steps, and architecture
- Enable/disable caching with the checkbox in the UI
- Drastically speeds up repeated experiments with the same settings

## Tips for Best Results

1. **Start with lower scale factors** (2x) for faster experimentation
2. **Scale-specific training steps**:
   - 2x: 1500-2000 steps
   - 4x: 3000 steps
   - 8x: 4000-5000 steps
3. **For 8x super-resolution**:
   - Use 4000-5000 training steps
   - Increase hidden layers to 4-5
   - Use 512 hidden features
   - Check quality metrics to verify results
4. **Use images with rich details** to see SIREN's strength in capturing high-frequency content
5. **Enable model cache** to avoid retraining with identical settings

## License

This demo is for educational purposes. Please cite the original SIREN paper if you use this in your work:

```bibtex
@inproceedings{sitzmann2020implicit,
    title={Implicit Neural Representations with Periodic Activation Functions},
    author={Sitzmann, Vincent and Martel, Julien NP and Bergman, Alexander W and Lindell, David B and Wetzstein, Gordon},
    booktitle={Proc. NeurIPS},
    year={2020}
}
```