Efficient representation of spatio-temporal data using cylindrical shearlets

TL;DR

This paper introduces a novel cylindrical shearlet-based multiscale representation for efficient modeling of 4D spatio-temporal data, outperforming traditional methods in approximation and dynamic tomography applications.

Contribution

The paper develops a new cylindrical shearlet system tailored for 4D data with hypersurface singularities, demonstrating superior approximation capabilities over existing multiscale representations.

Findings

Achieves better approximation of 4D spatio-temporal data.

Demonstrates improved performance in dynamic tomography.

Numerical results confirm the efficiency of the proposed method.

Abstract

Efficient representations of multivariate functions are critical for the design of state-of-the-art methods of data restoration and image reconstruction. In this work, we consider the representation of spatio-temporal data such as temporal sequences (videos) of 2- and 3-dimensional images, where conventional separable representations are usually very inefficient, due to their limitations in handling the geometry of the data. To address this challenge, we define a class $\mathcal{E}(A) \subset L^2(\mathbb{R}^4)$ of functions of 4 variables dominated by hypersurface singularities in the first three coordinates that we apply to model 4-dimensional data corresponding to temporal sequences (videos) of 3-dimensional objects. To provide an efficient representation for this type of data, we introduce a new multiscale directional system of functions based on cylindrical shearlets and prove that this new approach achieves superior approximation properties with respect to conventional multiscale representations. We illustrate the advantages of our approach by applying a discrete implementation of the new representation to a challenging problem from dynamic tomography. Numerical results confirm the potential of our novel approach with respect to conventional multiscale methods.