TY  - JOUR
AU  - Ceruso, S.
AU  - Bonaque-González, S.
AU  - Pareja-Ríos, A.
AU  - Carmona-Ballester, D.
AU  - Trujillo-Sevilla, J.
T1  - Reconstructing wavefront phase from measurements of its slope, an adaptive neural network based approach
LA  - eng
PY  - 2020
T2  - Optics and Lasers in Engineering
SN  - 0143-8166
VL  - 126
PB  - Elsevier Ltd
AB  - 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.
DO  - 10.1016/J.OPTLASENG.2019.105906
UR  - https://portalciencia.ull.es/documentos/5e39b34f2999523aa926f214
DP  - Dialnet - Portal de la Investigación
ER  -