Abstract
The long exposure point spread function (PSF) model is commonly used to improve signal-noise-ratio of astronomical object imaging and reduce the effect of atmospheric turbulence. In this paper, a move-and-superposition method of modeling the long exposure PSF based on Gaussian process is proposed. Experimental results show that the proposed modeling method can obtain more accurate estimation of final PSF for astronomical object imaging process than that of the simple shift-and-add PSF model.
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Fried, D.L.: Optical resolution through a randomly inhomogeneous medium for very long and very short exposures. J. Orient. Soc. Australia 56, 1372–1379 (1966)
Knox, K.T., Thompson, B.J.: Recovery of images from atmospherically degraded short-exposure photographs. Astrophys. J. 193, L45–L48 (1974)
Véran, J.P., Rigaut, F., Maître, H., Rouan, D.: Estimation of the adaptive optics long-exposure point-spread function using control loop data. J. Opt. Soc. Am. A 14, 3057–3069 (1997)
Marino, J.: Long exposure point spread function estimation from solar adaptive optics loop data. Ph.D. Dissertation, New Jersey Institute of Technology (2007)
Taylor, G.I.: The spectrum of turbulence. Proc. R. Soc. London A Math. Phys. Eng. Sci. 164, 476–490 (1938). The Royal Society Press
Zaman, K.B.M.Q., Hussain, A.K.M.F.: Taylor hypothesis and large-scale coherent structures. J. Fluid Mech. 112, 379–396 (1981)
Welsh, B.M.: Fourier-series-based atmospheric phase screen generator for simulating anisoplanatic geometries and temporal evolution. In: Optical Science, Engineering and Instrumentation 1997, pp. 327–338. International Society for Optics and Photonics Press (1997)
Sedmak, G.: Implementation of fast-Fourier-transform-based simulations of extra-large atmospheric phase and scintillation screens. Appl. Opt. 43, 4527–4538 (2004)
Von Karman, T.: Progress in the statistical theory of turbulence. Proc. Nat. Acad. Sci. U.S.A. 34, 530–539 (1948)
Yu, J., Yin, Q., Guo, P., Luo, A.: A deconvolution extraction method for 2D multi-object fibre spectroscopy based on the regularized least-squares QR-factorization algorithm. Mon. Not. R. Astron. Soc. 443, 1381–1389 (2014)
Luo, A., Zhang, H.T., Zhao, Y.H.: Data release of the LAMOST pilot survey. Res. Astron. Astrophys. 12, 1243 (2012)
Acknowledgments
The authors would like to thank Ms. Min Yan for her help in typesetting work of this manuscript. The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 61375045 and 61472043), the joint astronomic fund of the national natural science foundation of China and Chinese Academic Sinica (Project No. U1531242), and Beijing Natural Science Foundation (Project No. 4142030 and Project No. 4162027). Prof. Qian Yin and Ping Guo are the authors to whom all correspondence should be addressed.
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Guo, P., Yu, J., Yin, Q. (2016). Long Exposure Point Spread Function Modeling with Gaussian Processes. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_62
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DOI: https://doi.org/10.1007/978-3-319-40663-3_62
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