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Generalized sampling reconstruction from Fourier measurements using compactly supported shearlets
, 2014 Jackie Ma
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Embeddings of shearlet coorbit spaces into Sobolev spaces
, 2019 Hartmut Führ, René Koch
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Mask Responses for Single-Pixel Terahertz Imaging. [PDF]
Sci Rep, 2018 Augustin S +3 more
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Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data. [PDF]
Comput Biol Med, 2018 Baghaie A +5 more
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A New Deep Learning Based Multi-Spectral Image Fusion Method. [PDF]
Entropy (Basel), 2019 Piao J, Chen Y, Shin H.
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Edges and Corners With Shearlets
IEEE Transactions on Image Processing, 2015Shearlets are a relatively new and very effective multi-scale framework for signal analysis. Contrary to the traditional wavelets, shearlets are capable to efficiently capture the anisotropic information in multivariate problem classes. Therefore, shearlets can be seen as the valid choice for multi-scale analysis and detection of directional sensitive ...
DUVAL POO, MIGUEL ALEJANDRO +2 more
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Shearlets: Theory and Applications
GAMM-Mitteilungen, 2014AbstractMany important problem classes are governed by anisotropic features such as singularities concentrated on lower dimensional embedded manifolds, for instance, edges in images or shock fronts in solutions of transport dominated equations. While the ability to reliably capture and sparsely represent anisotropic structures is obviously the more ...
Wang-Q Lim +2 more
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Shearlets and pseudo-differential operators
Collectanea Mathematica, 2016Shearlets on the cone are a multi-scale and multi-directional discrete system that have near-optimal representation of the so-called cartoon-like functions. They form Parseval frames, have better geometrical sensitivity than traditional wavelets and an implementable framework.
Daniel Vera
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Parabolic Molecules: Curvelets, Shearlets, and Beyond
2014Anisotropic representation systems such as curvelets and shearlets have had a significant impact on applied mathematics in the last decade. The main reason for their success is their superior ability to optimally resolve anisotropic structures such as singularities concentrated on lower dimensional embedded manifolds, for instance, edges in images or ...
Sandra Keiper +3 more
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