# WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities

arXiv: https://arxiv.org/abs/2412.09775 · HTML: https://mlsanity.com/paper/2412.09775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model Adaptation for Inverse Problems in Imaging](https://mlsanity.com/paper/2012.00139/) (2020)
- [Trackastra: Transformer-based cell tracking for live-cell microscopy](https://mlsanity.com/paper/2405.15700/) (2024)
- [Coordinate-based neural representations for computational adaptive optics in widefield microscopy](https://mlsanity.com/paper/2307.03812/) (2023)
- [High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images](https://mlsanity.com/paper/1909.00023/) (2019)
- [Roadmap on Deep Learning for Microscopy](https://mlsanity.com/paper/2303.03793/) (2023)
- [Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction](https://mlsanity.com/paper/2206.08928/) (2022)
- [Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy](https://mlsanity.com/paper/2403.13214/) (2024)
- [Single-molecule orientation localization microscopy I: fundamental limits](https://mlsanity.com/paper/2010.04060/) (2020)
- [DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization](https://mlsanity.com/paper/2410.11281/) (2024)
- [Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope](https://mlsanity.com/paper/2203.14944/) (2022)

## Abstract (arXiv)

Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches.