README.md

---
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">

[![arXiv](https://img.shields.io/badge/arXiv-2412.09775-b31b1b.svg)](https://arxiv.org/abs/2412.09775)
[![GitHub](https://img.shields.io/badge/GitHub-mehta--lab%2Fwaveorder-181717?logo=github)](https://github.com/mehta-lab/waveorder)
[![Paper Page](https://img.shields.io/badge/Paper%20Page-Hugging%20Face-ff9d00?logo=huggingface)](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