Continuous curvelet transform: II. Discretization and frames

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Sparsity- and continuity-promoting seismic image recovery with curvelet frames (2008, background): “Curvelet frames, on the other hand, are designed to detect the wavefront set [6,8,9,11] and lead, as shown in Fig.”
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction (2015, background): “We refer the reader to [29] for constructions of functions φ,ψj satisfying (36) withA = B = 1, see [29, Theorem 4.”
Sparse multidimensional representation using shearlets (2005, background): “l=0 mk((B T )(ξ + αi))mk′((B T )k′ (ξ + αi′)) = δkk′δii′ (13)”
Non-parametric seismic data recovery with curvelet frames (2008, methodology): “This incapability explains why wavelets do not improve the compression rate for data with wavefronts as much as recently developed directional curvelet frames do (Candès and Donoho, 2000a, 2004; Candès and Donoho, 2005a,b).”
The Curvelet Transform (2010, background): “Therefore the curvelet construction is now based on the following two main ideas [11].”