# Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope

arXiv: https://arxiv.org/abs/2203.14944 · HTML: https://mlsanity.com/paper/2203.14944/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 67 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Lensless computational imaging through deep learning](https://mlsanity.com/paper/1702.08516/) (2017)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [Learning Sensor Multiplexing Design through Back-propagation](https://mlsanity.com/paper/1605.07078/) (2016)
- [Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks](https://mlsanity.com/paper/1909.06553/) (2019)
- [Reliable deep-learning-based phase imaging with uncertainty quantification](https://mlsanity.com/paper/1901.02038/) (2019)
- [Diffractive all-optical computing for quantitative phase imaging](https://mlsanity.com/paper/2201.08964/) (2022)
- [Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging](https://mlsanity.com/paper/1808.03571/) (2018)
- [An Optical Frontend for a Convolutional Neural Network](https://mlsanity.com/paper/1901.03661/) (2018)
- [Data-Driven Design for Fourier Ptychographic Microscopy](https://mlsanity.com/paper/1904.04175/) (2019)
- [Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy](https://mlsanity.com/paper/1906.03417/) (2019)
- [Convolutional neural networks that teach microscopes how to image](https://mlsanity.com/paper/1709.07223/) (2017)
- [Computer-free, all-optical reconstruction of holograms using diffractive networks](https://mlsanity.com/paper/2107.08177/) (2021)
- [Learning a Probabilistic Strategy for Computational Imaging Sensor Selection](https://mlsanity.com/paper/2003.10424/) (2020)
- [Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy](https://mlsanity.com/paper/2010.12970/) (2020)
- [Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Images](https://mlsanity.com/paper/2003.11004/) (2020)
- [Classification and reconstruction of spatially overlapping phase images using diffractive optical networks](https://mlsanity.com/paper/2108.07977/) (2021)
- [Physics-enhanced machine learning for virtual fluorescence microscopy](https://mlsanity.com/paper/2004.04306/) (2020)
- [Towards an Intelligent Microscope: adaptively learned illumination for optimal sample classification](https://mlsanity.com/paper/1910.10209/) (2019)
- [Multi-element microscope optimization by a learned sensing network with composite physical layers](https://mlsanity.com/paper/2006.15404/) (2020)

## Built on by (most cited first, 1 of 1)

- [WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities](https://mlsanity.com/paper/2412.09775/) (2024)

## Abstract (arXiv)

Designing new optical systems from the ground up for microscopy imaging tasks such as phase retrieval, requires substantial scientific expertise and creativity. To augment the traditional design process, we propose differentiable microscopy (partialμ), which introduces a top-down design approach. Using all optical phase retrieval as an illustrative example, we demonstrate the effectiveness of data-driven microscopy design through partialμ. Furthermore, we conduct comprehensive comparisons with existing all-optical phase retrieval methods, showcasing the consistent superiority of our learned designs across multiple datasets, including biological samples. To substantiate our ideas, we experimentally validate the functionality of one of the learned designs, providing a proof of concept. The proposed differentiable microscopy framework supplements the creative process of designing new phase microscopy systems and may be extended to other similar applications in optical design.