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Super-resolution method using sparse regularization for point-spread function recovery

Abstract : 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.
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Submitted on : Thursday, March 17, 2016 - 3:49:06 PM
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F. M. Ngolè Mboula, J.-L. Starck, S. Ronayette, K. Okumura, J. Amiaux. Super-resolution method using sparse regularization for point-spread function recovery. Astronomy and Astrophysics - A&A, EDP Sciences, 2015, 575, pp.A86. ⟨10.1051/0004-6361/201424167⟩. ⟨cea-01290110⟩



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