Teaching astronomical speckle techniques

Aime, Claude

doi:10.1088/0143-0807/22/2/309

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Due to the short exposure time, the photo-events (a few hundred per image) are assumed to be independent and follow Poisson statistics. Consequently, it can be shown (Aime 2001) that the detected autocorrelation origin of the image takes into account a supplementary term, which is inversely proportional to the mean photo-events per image N. The power spectrum obtained is biased by a additional term 1/N for all frequencies. We change the autocorrelation center value to correct it.…”

Section: Error Estimationmentioning

confidence: 98%

Wavefront outer scale deduced from interferometric dispersed fringes

Maire¹,

Ziad²,

Borgnino³

et al. 2006

A&A

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In addition to site characterization, measurements of critical atmospheric parameters are required to design and to optimize future adaptive optic systems and long-baseline interferometers. It is possible to estimate seeing conditions by processing data obtained with existing High Angular Resolution instruments. We report the results of joint observations with the GI2T interferometer and the GSM site-testing monitor performed over a period of several nights. We compared estimates of the wavefront outer scale done at various baselines as well as the seeing (Fried's parameter). We processed interferometric data by calculating power spectra of dispersed fringe images. Deduced measurements of the optical path difference lead to the estimates of the outer scale. We found that the outer scale values obtained from the GI2T data are mostly in the 5−30 m range, in good agreement with GSM measurements.

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Section: Error Estimationmentioning

confidence: 98%

Wavefront outer scale deduced from interferometric dispersed fringes

Maire¹,

Ziad²,

Borgnino³

et al. 2006

A&A

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“…The point spread function (PSF) is a realistic representation of the PSF of a true telescope. The principle of the simulation is given in [1] and can be summarized as follows. The telescope aperture P(r ) is simply given by an array of points of value '1' inside a circle and '0' outside.…”

Section: Numerical Illustrationsmentioning

confidence: 99%

Penalized maximum likelihood image restoration with positivity constraints: multiplicative algorithms

2002

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In this paper, we propose a general method to devise maximum likelihood penalized (regularized) algorithms with positivity constraints. Moreover, we explain how to obtain ‘product forms’ of these algorithms. The algorithmic method is based on Kuhn–Tucker first-order optimality conditions. Its application domain is not restricted to the cases considered in this paper, but it can be applied to any convex objective function with linear constraints. It is specially adapted to the case of objective functions with a bounded domain, which completely encloses the domain of the (linear) constraints. The Poisson noise case typical of this last situation and the Gaussian additive noise case are considered and they are associated with various forms of regularization functions, mainly quadratic and entropy terms. The algorithms are applied to the deconvolution of synthetic images blurred by a realistic point spread function similar to that of Hubble Space Telescope operating in the far-ultraviolet and corrupted by noise. The effect of the relaxation on the convergence speed of the algorithms is analysed. The particular behaviour of the algorithms corresponding to different forms of regularization functions is described. We show that the ‘prior’ image is a key point in the regularization and that the best results are obtained with Tikhonov regularization with a Laplacian operator. The analysis of the Poisson process and of a Gaussian additive noise leads to similar conclusions. We bring to the fore the close relationship between Tikhonov regularization using derivative operators, and regularization by a distance to a ‘default image’ introduced by Horne (Horne K 1985 Mon. Not. R. Astron. Soc. 213 129–41).

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Imaging via Speckle Interferometry and Nonlinear Methods

Sadler¹,

Maev²

2013

Advances in Acoustic Microscopy and High Resolution Imaging

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Teaching astronomical speckle techniques

Cited by 5 publications

References 18 publications

Wavefront outer scale deduced from interferometric dispersed fringes

Wavefront outer scale deduced from interferometric dispersed fringes

Penalized maximum likelihood image restoration with positivity constraints: multiplicative algorithms

Imaging via Speckle Interferometry and Nonlinear Methods

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