mlsanityData to 14 Oct 2026

Differentiable model-based adaptive optics for two-photon microscopy

arXiv 2104.14308 ↗First posted 29 Apr 2021physics.optics cs.LGcited by 2

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

  1. 2015Automatic differentiation in machine learning: a surveycited by 855
  2. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
  3. 2018SPNets: Differentiable Fluid Dynamics for Deep Neural Networkscited by 62
  4. 2020Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural networkcited by 24
  5. 2019Data-Driven Design for Fourier Ptychographic Microscopycited by 18
  6. 2019Diffraction tomography with a deep image priorcited by 15
  7. 2017Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimationcited by 11
  8. 2020Differentiable model-based adaptive optics with transmitted and reflected lightcited by 2
  9. 2020Adaptive optics with reflected light and deep neural networkscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 2.

2 papers.