Differentiable model-based adaptive optics for two-photon microscopy
Not on the map. Why?
Abstract (arXiv)
Aberrations limit scanning fluorescence microscopy when imaging in scattering materials such as biological tissue. Model-based approaches for adaptive optics take advantage of a computational model of the optical setup. Such models can be combined with the optimization techniques of machine learning frameworks to find aberration corrections, as was demonstrated for focusing a laser beam through aberrations onto a camera [arXiv:2007.13400]. Here, we extend this approach to two-photon scanning microscopy. The developed sensorless technique finds corrections for aberrations in scattering samples and will be useful for a range of imaging application, for example in brain tissue.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 42 references that are in the map. First posted on arXiv 29 Apr 2021.
Builds on
9 of 42 references.
References · 9
- 2015Automatic differentiation in machine learning: a surveycited by 855
- 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
- 2018SPNets: Differentiable Fluid Dynamics for Deep Neural Networkscited by 62
- 2020Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural networkcited by 24
- 2019Data-Driven Design for Fourier Ptychographic Microscopycited by 18
- 2019Diffraction tomography with a deep image priorcited by 15
- 2017Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimationcited by 11
- 2020Differentiable model-based adaptive optics with transmitted and reflected lightcited by 2
- 2020Adaptive optics with reflected light and deep neural networkscited by 1
Built on by
Citing papers per year: 2022: 2.
2 papers.