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The Curvelet Transform

Abstract : Multiresolution methods are deeply related to image processing, biological and computer vision, and scientific computing. The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges. It has generated increasing interest in the community of applied mathematics and signal processing over the years. In this article, we present a review on the curvelet transform, including its history beginning from wavelets, its logical relationship to other multiresolution multidirectional methods like contourlets and shearlets, its basic theory and discrete algorithm. Further, we consider recent applications in image/video processing, seismic exploration, fluid mechanics, simulation of partial different equations, and compressed sensing.
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Contributeur : Pascale Nalon <>
Soumis le : jeudi 20 janvier 2011 - 11:44:50
Dernière modification le : jeudi 24 septembre 2020 - 16:34:08


  • HAL Id : hal-00557892, version 1


Jianwei Ma, Gerlind Plonka. The Curvelet Transform. Signal Processing Magazine, 2010, 27 (2), pp.118-133. ⟨hal-00557892⟩



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