Maximum-likelihood estimation of an astronomical image from a sequence at low photon levels.

Astronomy and Astrophysics – Astronomy

Scientific paper

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Image Reconstruction

Scientific paper

The authors consider the problem of estimating one nonblurred and cleaned image from a sequence of P randomly translated images corrupted with Poisson noise. They develop a new algorithm based on maximum-likelihood (ML) estimation for two unknown parameters: the reconstructed image itself and the set of translations of the low-light-level images. The authors demonstrate that the ML reconstructed image is proportional to the sum of the low-light-level images after correcting for the unknown movement and that its entropy is minimal. The images of the sequence are matched together by means of an iterative minimum-entropy algorithm, where a systematic search under displacements for the images is performed. The authors develop a fast version of this algorithm, and they present results for simulated images and experimental data. This approach is applied to astronomical images that are acquired by photocounting from a balloon-borne ultraviolet imaging telescope.

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