Leiden University Student Repository

Landman, Rico (2018)

Wavefront Reconstruction with Convolutional Neural Networks for the Generalised Optical Differentiation Wavefront Sensor

Bachelor thesis | Natuurkunde (BSc)

In this thesis paper the possibility of using Convolutional Neural Networks (CNN) for wavefront reconstruction is studied. This is tested with the Generalised Optical Differentiation Wavefront Sensor (g-ODWFS), a nonlinear pupil plane wavefront sensor that uses spatially varying polarisation rotators in the focal plane. Network architectures are developed for modal reconstruction in the Zernike and actuator basis. Both simulations and experimental work show that using a CNN for wavefront reconstruction improves the estimation of large phase aberrations as compared to the currently used linear method. This increases the convergence rate of the adaptive optics system, which is demonstrated in closed loop simulations.

Adaptive Optics

Wavefront Sensing

Wavefront Reconstruction

Atmospheric Turbulence

Deep Learning

Convolutional Neural Networks

Author

Landman, Rico

Note

Evaluation: 4

Faculty

Faculty of Science

Supervisors

Keller, Christoph

ECTS Credits

3

Language

en