Sparse Approximation of Computational Time Reversal Imaging

TL;DR

This paper introduces a sparse approximation method for computational time reversal imaging that enhances localization, denoising, and scattering coefficient estimation in noisy environments, all formulated in the frequency domain.

Contribution

It presents a novel sparse approximation approach for time reversal imaging, improving robustness and capabilities in noise and coefficient estimation.

Findings

Effective in localizing multiple scatterers

Enhances denoising in imaging process

Accurately estimates scattering coefficients under noise

Abstract

Computational time reversal imaging can be used to locate the position of multiple scatterers in a known background medium. Here, we discuss a sparse approximation method for computational time-reversal imaging. The method is formulated entirely in the frequency domain, and besides imaging it can also be used for denoising, and to determine the magnitude of the scattering coefficients in the presence of moderate noise levels.