The Python implementation of LIFT, a technique initially presented in [1]. The code is optimized for both CPU and GPU. For a detailed example of usage please refer to 'tests/example.ipynb'.
numpy
scipy
cupy (optional, to enable GPU acceleration)
pillow (optional)
photutils (optional)
matplotlib (optional)
skimage (optional)
This code was developed during a master's thesis internship and a PhD program of Arseniy Kuznetsov, funded by ESO, LAM, and ONERA. The original architecture of the code is inspired by the OOPAO (see https://github.com/cheritier/OOPAO). Some parts of the Zernike modal basis implementation are copied from AOtools (see https://github.com/AOtools/aotools)
[1] S. Meimon, T. Fusco, and L. M. Mugnier, LIFT: a focal-plane wavefront sensor for real-time low-order sensing on faint sources, Opt. Lett., vol. 35, no. 18, p. 3036, Sep. 2010, doi: 10.1364/OL.35.003036.
[2] C. Plantet, S. Meimon, J.-M. Conan, and T. Fusco, Experimental validation of LIFT for estimation of low-order modes in low-flux wavefront sensing, Opt. Express, vol. 21, no. 14, p. 16337, Jul. 2013, doi: 10.1364/OE.21.016337.
[3] A. Kuznetsov, S. Oberti, C. Heritier, C. Plantet, B. Neichel, T. Fusco, S. Ströbele, C. Correia, Study of the LIFT focal-plane wavefront sensor for GALACSI NFM, Proceedings SPIE Adaptive Optics Systems VIII, Jul. 2022, Montréal, Canada. pp.109, hal-03796122.
[4] G. Agapito, L. Busoni, G. Carlà, C. Plantet, S. Esposito, and P. Ciliegi, MAORY/MORFEO and LIFT: can the low order wavefront sensors become phasing sensors?, in Adaptive Optics Systems VIII, Aug. 2022, p. 195, doi: 10.1117/12.2629352.
This project is licensed under the terms of the MIT license.