文章基本信息

标题：Learning sparse representations on the sphere
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作者：F. Sureau ; F. Voigtlaender ; M. Wust 等
期刊名称：Astronomy & Astrophysics
印刷版ISSN：0004-6361
电子版ISSN：1432-0746
出版年度：2019
卷号：621
DOI：10.1051/0004-6361/201834041
语种：English
出版社：EDP Sciences
摘要：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, theα-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.
关键词：enmethods: data analysismethods: statisticalmethods: numerical