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---
title: WaveOrder
emoji: 🔬
python_version: 3.13
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.0.2
app_file: app.py
pinned: false
license: bsd-3-clause
tags:
- microscopy
- computational-imaging
- phase-reconstruction
- bioimaging
- scientific-visualization
---
# WaveOrder
<div align="center">
[](https://arxiv.org/abs/2412.09775)
[](https://github.com/mehta-lab/waveorder)
[](https://huggingface.co/papers/2412.09775)
</div>
## 📄 Paper
**WaveOrder: generalist framework for label-agnostic computational microscopy**
Chandler T., Ivanov I.E., Hirata-Miyasaki E., et al. "WaveOrder: Physics-informed ML for auto-tuned multi-contrast computational microscopy from cells to
organisms." [arXiv:2412.09775](https://arxiv.org/abs/2412.09775) (2025)
## 🔬 About
Interactive web interface for exploring phase reconstruction from quantitative label-free microscopy data. This demo showcases the WaveOrder framework's capabilities for reconstructing phase contrast images with interactive parameter optimization.
### Features
- **Interactive Visualization**: Side-by-side comparison of raw and reconstructed phase images
- **Real-time Parameter Tuning**: Adjust reconstruction parameters and see results instantly
- **Automated Optimization**: Gradient-based optimization to find optimal reconstruction parameters
- **GPU Acceleration**: 15-25× speedup with CUDA-capable devices (auto-detected)
- **Multi-FOV Support**: Navigate through multiple fields of view from plate imaging
### Reconstruction Parameters
- **Z Offset**: Axial focus calibration
- **Numerical Apertures**: Detection and illumination NA optimization
- **Tilt Angles**: Zenith and azimuthal illumination tilt correction
## 🚀 Usage
1. **Select Field of View**: Choose from available FOVs in the dropdown
2. **Navigate Z-stack**: Use the Z-slice slider to explore different focal planes
3. **Optimize Parameters**: Click "⚡ Optimize Parameters" to automatically find optimal settings
4. **Manual Reconstruction**: Adjust sliders manually and click "🔬 Run Reconstruction"
5. **Review Results**: Scrub through optimization iterations to see parameter evolution
## 📊 Dataset
This demo uses concatenated 20x objective microscopy data from high-content screening plates, featuring brightfield phase contrast imaging.
## 🔗 Links
- **Paper**: [arXiv:2412.09775](https://arxiv.org/abs/2412.09775)
- **GitHub Repository**: [mehta-lab/waveorder](https://github.com/mehta-lab/waveorder)
- **Documentation**: [WaveOrder Docs](https://github.com/mehta-lab/waveorder/tree/main/docs)
## 📝 Citation
```bibtex
@misc{chandler2024waveordergeneralistframeworklabelagnostic,
title={waveOrder: generalist framework for label-agnostic computational microscopy},
author={Talon Chandler and Eduardo Hirata-Miyasaki and Ivan E. Ivanov and Ziwen Liu and Deepika Sundarraman and Allyson Quinn Ryan and Adrian Jacobo and Keir Balla and Shalin B. Mehta},
year={2024},
eprint={2412.09775},
archivePrefix={arXiv},
primaryClass={physics.optics},
url={https://arxiv.org/abs/2412.09775},
}
```
## ⚖️ License
BSD 3-Clause License |