Differentiable Microscopy Designs an All Optical Phase Retrieval Microscope
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
24 of 67 references.
References · 24
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2017Deep Complex Networkscited by 217
- 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 177
- 2017Phase recovery and holographic image reconstruction using deep learning in neural networkscited by 54
- 2017Lensless computational imaging through deep learningcited by 44
- 2018PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningcited by 41
- 2016Learning Sensor Multiplexing Design through Back-propagationcited by 28
- 2019Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networkscited by 27
- 2019Reliable deep-learning-based phase imaging with uncertainty quantificationcited by 26
- 2022Diffractive all-optical computing for quantitative phase imagingcited by 22
- 2018Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imagingcited by 18
12 more
- 2018An Optical Frontend for a Convolutional Neural Networkcited by 18
- 2019Data-Driven Design for Fourier Ptychographic Microscopycited by 18
- 2019Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracycited by 15
- 2017Convolutional neural networks that teach microscopes how to imagecited by 13
- 2021Computer-free, all-optical reconstruction of holograms using diffractive networkscited by 12
- 2020Learning a Probabilistic Strategy for Computational Imaging Sensor Selectioncited by 8
- 2020Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopycited by 8
- 2020Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Imagescited by 6
- 2021Classification and reconstruction of spatially overlapping phase images using diffractive optical networkscited by 5
- 2020Physics-enhanced machine learning for virtual fluorescence microscopycited by 4
- 2019Towards an Intelligent Microscope: adaptively learned illumination for optimal sample classificationcited by 3
- 2020Multi-element microscope optimization by a learned sensing network with composite physical layerscited by 3
Built on by
Citing papers per year: 2024: 1.
1 paper.