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Nonseparable Shearlet Transform
IEEE Transactions on Image Processing, 2013Over the past few years, various representation systems which sparsely approximate functions governed by anisotropic features, such as edges in images, have been proposed. Alongside the theoretical development of these systems, algorithmic realizations of the associated transforms are provided.
Wang-q Lim
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A Shearlet Approach to Edge Analysis and Detection [PDF]
IEEE Transactions on Image Processing, 2009 It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges.
Demetrio Labate
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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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IEEE Transactions on Image Processing, 2009
In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets.
Vishal M. Patel +2 more
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In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets.
Vishal M. Patel +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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Oberwolfach Reports, 2011
Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means for encoding isotropic phenomena. Directional multiscale systems, particularly shearlets, are now having the same dramatic impact on the encoding of multivariate signals.
Demetrio Labate, Gitta Kutyniok
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Over the last 20 years, multiscale methods and wavelets have revolutionized the field of applied mathematics by providing an efficient means for encoding isotropic phenomena. Directional multiscale systems, particularly shearlets, are now having the same dramatic impact on the encoding of multivariate signals.
Demetrio Labate, Gitta Kutyniok
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Clifford Valued Shearlet Transform
Advances in Applied Clifford Algebras, 2020This paper deals with the construction of $$n=3 \text{ mod } 4$$ Clifford algebra $$Cl_{n,0}$$ -valued admissible shearlet transform using the shearlet group $$(\mathbb {R}^* < imes \mathbb {R}^{n-1}) < imes \mathbb {R}^n$$ , a subgroup of affine group of $${\mathbb {R}}^n$$ .
Jyoti Sharma, Shivam Kumar Singh
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Shearlets and their applications
AIP Conference Proceedings, 2012Shearlets were introduced as means to sparsely encode anisotropic singularities of multivariate data while providing a unified treatment of the continuous and digital realm. In this chapter, recent results on compactly supported shearlet systems will be presented, in particular, showing that these shearlet frames provide optimally sparse approximations
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Shearlets as Multi-scale Radon Transforms
Sampling Theory in Signal and Image Processing, 2017We show that the 2D-shearlet transform is the composition of the affine Radon transform, a 1D-wavelet transform and a 1D-convolution.
BARTOLUCCI, FRANCESCA +3 more
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