Astronomical image reconstruction is an inverse problem based on the knowledge of the point-spread function (PSF). However, this knowledge is often only partial, and a myopic deconvolution process is required for reaching the estimation of the solution. In this paper we propose a new statistical model which incorporates the presence of noise both on the image of the object to retrieve and on the 'measured' PSF. This technique also takes into account the nonnegativity constraint on the solution and on the PSF. Deconvolution results are presented for simulated data. A comparison between the classical algorithms and that proposed in this paper is given. This method can also be extended when different measures of PSF with different sizes are available.
Joint myopic deconvolution
Benvenuto, F.;
2010-01-01
Abstract
Astronomical image reconstruction is an inverse problem based on the knowledge of the point-spread function (PSF). However, this knowledge is often only partial, and a myopic deconvolution process is required for reaching the estimation of the solution. In this paper we propose a new statistical model which incorporates the presence of noise both on the image of the object to retrieve and on the 'measured' PSF. This technique also takes into account the nonnegativity constraint on the solution and on the PSF. Deconvolution results are presented for simulated data. A comparison between the classical algorithms and that proposed in this paper is given. This method can also be extended when different measures of PSF with different sizes are available.
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