Shearlets: an overview

Abstract

The aim of this report is a self-contained overview on shearlets, a new multiscale method emerged in the last decade to overcome some of the limitation of traditional multiscale methods, like wavelets. Shearlets are obtained by translating, dilating and shearing a single mother function. Thus, the elements of a shearlet system are distributed not only at various scales and locations – as in classical wavelet theory – but also at various orientations. Thanks to this directional sensitivity property, shearlets are able to capture anisotropic features, like edges, that frequently dominate multidimensional phenomena, and to obtain optimally sparse approximations. Moreover, the simple mathematical structure of shearlets allows for the generalization to higher dimensions and to treat uniformly the continuum and the discrete realms, as well as fast algorithmic implementation. For all these reasons, shearlets are one of the most successful tool for the efficient representation of multidimensional data and they are being employed in several numerical applications