Reconstructing wavefront phase from measurements of its slope, an adaptive neural network based approach
Abstract
We present a neural network based method to integrate wave front shapes from its slopes. Tests were done in order to verify and compare its performance with other state of the art finite-difference least squares methods, proving that better precision can be achieved having representative data to train our model, even when there exist noise in the data. Also, a simple simulation of the Gran Telescopio de Canarias telescope was carried out, finding that the results provided by our method ranged from ≈ 5 to ≈ 19 times better than other ones in this case. Finally, the source code of our method has been incorporated into a public repository with the objective that it can be tested and used by any other researcher.
- Publication:
-
Optics and Lasers in Engineering
- Pub Date:
- March 2020
- DOI:
- 10.1016/j.optlaseng.2019.105906
- Bibcode:
- 2020OptLE.12605906C
- Keywords:
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- Neural network;
- Shape reconstruction;
- Wave front integration