Volume 575, March 2015
|Number of page(s)||11|
|Section||Numerical methods and codes|
|Published online||02 March 2015|
Super-resolution method using sparse regularization for point-spread function recovery
Laboratoire AIM, UMR CEA-CNRS-Paris 7, Irfu, Service d’Astrophysique, CEA
Received: 9 May 2014
Accepted: 3 September 2014
In large-scale spatial surveys, such as the forthcoming ESA Euclid mission, images may be undersampled due to the optical sensors sizes. Therefore, one may consider using a super-resolution (SR) method to recover aliased frequencies, prior to further analysis. This is particularly relevant for point-source images, which provide direct measurements of the instrument point-spread function (PSF). We introduce SParse Recovery of InsTrumental rEsponse (SPRITE), which is an SR algorithm using a sparse analysis prior. We show that such a prior provides significant improvements over existing methods, especially on low signal-to-noise ratio PSFs.
Key words: techniques: image processing / methods: numerical
© ESO, 2015
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