Advanced Imaging Methods for Long-Baseline Optical Interferometry

ONERA, Chatillon
IEEE Journal of Selected Topics in Signal Processing (Impact Factor: 2.37). 11/2008; 2(5):767 - 780. DOI: 10.1109/JSTSP.2008.2005353
Source: IEEE Xplore


We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, Mira and Wisard, representative of the two generic approaches for dealing with the missing phase information. Mira is based on an implicit approach and a direct optimization of a Bayesian criterion while Wisard adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called ldquosoft support constraintrdquo that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging.

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    • "Clearly, the phase closure allows to get rid of atmospheric effects for triplets of complex visibilities. If N t denotes the number of telescopes there are (N t − 1)(N t − 2)/2 independent closure phases [33]. Let matrix H λn concatenate in its rows the independent closure phases of the type (13) that can be obtained for the available triplets of telescopes at wavelength λ n . "
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    Full-text · Article · Jun 2014 · Journal of the Optical Society of America A
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    • "Wisard takes a self-calibration approach to explicitly solve for missing Fourier phase information; whereas Mira which only use forward modeling (that is, from the image parameters to the data) implicitly take into account any lack of information. A more detailed comparison of the two algorithms has been done by Le Besnerais et al. (2008). 3.6. "
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