Wavefront sensor optimisation with ridgelets for astronomical image restoration

Specific wavefront aberrations cannot be directly measured from an image. A wavefront sensor can use intensity variations from a point source to estimate wavefront aberration. However, processing can be computationally intensive and this is a challenge for real-time image restoration. A relatively simple wavefront sensor (WFS) can be implemented using two focal plane images. Typically, measured aberration data from these sensors are used in closed-loop control, where a conjugate of the aberration using a deformable mirror (DM) is applied to the optical path for compensation. However, closed-loop adaptive optics systems are hardware intensive. On the other hand, open-loop control simplifies the overall system, but computational demands are considerably higher. In this paper, we discuss how the ridgelet method, which has a broad application in signal analysis, is used with the geometric wavefront sensor to enhance performance in an open-loop configuration.

[1]  Jean-luc Starck,et al.  Multiscale Methods in Astronomy: Beyond Wavelets , 2002 .

[2]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[4]  David L. Donoho,et al.  Digital curvelet transform: strategy, implementation, and experiments , 2000, SPIE Defense + Commercial Sensing.

[5]  E. Candès,et al.  Ridgelets: a key to higher-dimensional intermittency? , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Jean-Luc Starck,et al.  Astronomical Data Analysis and Sparsity: From Wavelets to Compressed Sensing , 2009, Proceedings of the IEEE.

[7]  Gerlind Plonka-Hoch,et al.  The Curvelet Transform , 2010, IEEE Signal Processing Magazine.

[8]  Marcos A. van Dam,et al.  Direct wavefront sensing using geometric optics , 2002, SPIE Optics + Photonics.

[9]  R. Noll Zernike polynomials and atmospheric turbulence , 1976 .

[10]  Francois Roddier,et al.  Adaptive Optics in Astronomy: Imaging through the atmosphere , 2004 .

[11]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[12]  E. Candès,et al.  Astronomical image representation by the curvelet transform , 2003, Astronomy & Astrophysics.

[13]  Richard G. Lane,et al.  Estimating phase aberrations from intensity data , 2003 .

[14]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  A. Kolmogorov The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers , 1991, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

[16]  Yan Li,et al.  Image Classification Using Wavelet Coefficients in Low-pass Bands , 2007, 2007 International Joint Conference on Neural Networks.