Learning sparse representations on the sphere - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Journal Articles Astronomy and Astrophysics - A&A Year : 2019

Learning sparse representations on the sphere


Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to extract a specific set of features, and choosing the best fixed representation system for a given scientific application is challenging. In this paper, we show that one can directly learn a representation system from given data on the sphere. We propose two new adaptive approaches: the first is a (potentially multiscale) patch-based dictionary learning approach, and the second consists in selecting a representation from among a parametrized family of representations, the α-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.
Fichier principal
Vignette du fichier
aa34041-18.pdf (6.05 Mo) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

cea-03012045 , version 1 (18-11-2020)



F. Sureau, F. Voigtlaender, M. Wust, J.-L. Starck, G. Kutyniok. Learning sparse representations on the sphere. Astronomy and Astrophysics - A&A, 2019, 621, pp.A73. ⟨10.1051/0004-6361/201834041⟩. ⟨cea-03012045⟩
23 View
34 Download



Gmail Facebook Twitter LinkedIn More