The construction of continuous shearlet transform has been extended to higher dimensions. It was generalized a group that is topologically isomorphic semidirect product locally compact groups. In this paper, by unified theoretical linear algebra approach the representation theory, class transforms obtained from quasi-regular presented. order develop such representation, we utilize homogeneous s...

Watermarking is an operation to hide important information. In this paper, a new watermarking algorithm using Shearlet transform and GWO optimization algorithm as well as SVD transform is presented. The results of this paper show the improvement of robustness and transparency of the new algorithm.

We prove that a (globally) subanalytic function f : X ⊂ Qp → Qp which is locally Lipschitz continuous with some constant C is piecewise (globally on each piece) Lipschitz continuous with possibly some other constant, where the pieces can be taken to be subanalytic. We also prove the analogous result for a subanalytic family of functions fy : Xy ⊂ Qp → Qp depending on p-adic parameters. The stat...

Title: Abstract: I will discuss a new decomposition of the 3D X-ray transform based on the shearlet representation, a multiscale directional representation which is optimally efficient in handling 3D data containing edge singularities. This decomposition yields a highly effective reconstruction algorithm providing a near-optimal rate of convergence in estimating piecewise smooth objects from 3D...

Convolutional Neural Networks (CNNs) are a type of deep neural network which have performed well at image and audio classification. One approach to understanding the success of CNNs is Mallat’s scattering transform, which formalizes the observation that the filters learned by a CNN have wavelet-like structure. The resulting transform generates a representation that is approximately translation ...

The fast digital shearlet transform (FDST) was recently introduced as a means to analyze natural images efficiently, owing to the fact that those are typically governed by cartoon-like structures. In this paper, we introduce and discuss a first-order hybrid sigma-delta quantization algorithm for coarsely quantizing the shearlet coefficients generated by the FDST. Radial oversampling in the freq...

It is a challenging task to reconstruct images from their noisy, blurry, and/or incomplete measurements, especially those with important details and features such as medical magnetic resonance (MR) and CT images. We propose a novel regularization model that integrates two recently developed regularization tools: total generalized variation (TGV) by Bredies, Kunisch, and Pock; and shearlet trans...

In allusion to remove Racian noise while lessen the loss of details as low as possible, this paper proposed an filter algorithm which comprehensive utilize Multi-Objective Genetic Algorithm (MOGA) and Shearlet transform based on a Multi-scale Geometric Analysis (MGA) theory. First, it performs a wavelet multi-scale decomposition of image. Then, it builds target function in MOGA by several evalu...