Machine learning for image-based wavefront sensing

Astronomical images are often degraded by the disturbance of the Earth’s atmosphere. This thesis proposes to improve image-based wavefront sensing techniques using machine learning algorithms. Deep convolutional neural networks (CNN) have thus been trained to estimate the wavefront using one or multiple intensity measurements.

Getting Started

Prerequisites

First, make sure the following python libraries are installed.

Aotools
Astropy
Soapy
Scipy
Pytorch
Visdom

Examples

The dataset generation can be run using. The dataset size and other parameters can be set in the same file.

python src/generation/generator.py

Some notebooks to highlights the networks and the dataset.

• Overview of the dataset
• Network Training
• Network evaluation

Finally some classical algorithms (Gerchberg–Saxton) can be directly tested on the dataset.

python src/algorithms/Gerchberg–Saxton.py