Optimal Compressive Imaging of Fourier Data

TL;DR

This paper introduces an optimal compressive imaging scheme for Fourier data, combining anisotropic sampling and shearlet-based reconstruction, achieving asymptotic optimality for cartoon-like images and demonstrating superior numerical performance.

Contribution

It presents a novel sampling and reconstruction method using anisotropic sampling and shearlet frames, proving asymptotic optimality for cartoon-like functions.

Findings

Scheme outperforms existing methods in numerical experiments

Achieves asymptotic optimality for cartoon-like functions

Combines anisotropic sampling with shearlet-based reconstruction

Abstract

Applications such as Magnetic Resonance Tomography acquire imaging data by point samples of their Fourier transform. This raises the question of balancing the efficiency of the sampling strategies with the approximation accuracy of an associated reconstruction procedure. In this paper, we introduce a novel sampling-reconstruction scheme based on a random anisotropic sampling pattern and a compressed sensing type reconstruction strategy with a variant of dualizable shearlet frames as sparsifying representation system. For this scheme, we prove asymptotic optimality in an approximation theoretic sense for cartoon-like functions as a model class for the imaging data. Finally, we present numerical experiments showing the superiority of our scheme over other approaches.