Results 141 to 150 of about 640 (174)
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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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A new construction of shearlets
Infinite Dimensional Analysis, Quantum Probability and Related Topics, 2022In order to achieve optimally sparse approximations of signals exhibiting anisotropic singularities, the shearlet systems that are systems of functions generated by one generator with dilation, shear transformation and translation operators applied to it were introduced.
Pooran Ghaderihasab, Ahmad Ahmadi
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Clifford Valued Shearlet Transform
Advances in Applied Clifford Algebras, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sharma, Jyoti, Singh, Shivam Kumar
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SHEARLET TRANSFORMS AND DIRECTIONAL REGULARITIES
International Journal of Wavelets, Multiresolution and Information Processing, 2010In an effort to characterize uniform and pointwise Hölder regularities, we obtain necessary decay rates and sufficient decay rates of continuous and discrete shearlet transform across scales. They are the same rates as those of the Hart Smith and continuous curvelet transforms.
P. Lakhonchai +2 more
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2018 26th European Signal Processing Conference (EUSIPCO), 2018
In video coding, in-loop filtering has attracted attention due to its increasing coding performances. In this paper the shearlet-based loop filter is proposed using a sparsifying transform, the shearlet transform, which can identify the important structures of natural images such as edges in the sparse transform domain.
Johannes Erfurt +4 more
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In video coding, in-loop filtering has attracted attention due to its increasing coding performances. In this paper the shearlet-based loop filter is proposed using a sparsifying transform, the shearlet transform, which can identify the important structures of natural images such as edges in the sparse transform domain.
Johannes Erfurt +4 more
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Shearlet Features for Pedestrian Detection
Journal of Mathematical Imaging and Vision, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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, 2017It is shown that the 2D-shearlet transform is the composition of the affine Radon transform, a 1D-wavelet transform and a 1D-convolution. This is applied to give an alternative proof of the fact that the shearlet transform is able to resolve the wavefront set associated with the unit disc.
BARTOLUCCI, FRANCESCA +3 more
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The Discrete Shearlet Transform: A New Directional Transform and Compactly Supported Shearlet Frames
IEEE Transactions on Image Processing, 2010It is now widely acknowledged that analyzing the intrinsic geometrical features of the underlying image is essential in many applications including image processing. In order to achieve this, several directional image representation schemes have been proposed.
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A Shearlet Approach to Edge Analysis and Detection
IEEE Transactions on Image Processing, 2009It 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.
Sheng Yi +3 more
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