Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging

TL;DR

This paper develops sparse signal recovery techniques for ultrasonic array imaging of two-layer objects, modeling wave propagation and comparing greedy algorithms with $ ext{l}_1$-norm minimization, demonstrating superior performance over traditional methods.

Contribution

It introduces sparse signal representation methods for ultrasonic imaging, extending to unknown wave velocities, and compares computational and accuracy aspects of different algorithms.

Findings

$ ext{l}_1$-norm minimization outperforms greedy algorithms.

Sparse techniques outperform conventional imaging methods.

Methods successfully applied to experimental data.

Abstract

In this paper, we model the signal propagation effect in ultrasonic imaging using Huygens principle and use this model to develop sparse signal representation based imaging techniques for a two-layer object immersed in water. Relying on the fact that the image of interest is sparse, we cast such an array based imaging problem as a sparse signal recovery problem and develop two types of imaging methods, one method uses only one transducer to illuminate the region of interest {and for this case the system is modeled as a single input multiple output (SIMO) system. The second method relies on all transducers to transmit ultrasonic waves into the material under test and in this case the system is modeled as a multiple input multiple output (MIMO) system}. We further extend our work to a scenario where the propagation velocity of the wave in the object under test is not known precisely. {We discuss different techniques such as greedy based algorithms as well as $\ell_1$-norm minimization based approach to solve the proposed sparse signal representation based method. We give an assessment of the computational complexity of the $\ell_1$-norm minimization based approach for the SIMO and the MIMO cases. We further point out the superiority of the $\ell_1$-norm minimization based approach over the greedy based algorithms. Then we give a comprehensive analysis of error for both the greedy based approaches as well as the $\ell_1$-norm minimization based technique for both the SIMO and the MIMO cases. The analysis utilizes tools from two powerful branches of modern analysis, \emph{local analysis in Banach spaces} and \emph{concentration of measure}}. We finally apply our methods to experimental data gathered from a solid test sample immersed in water and show that sparse signal recovery based techniques outperform the conventional methods available in the literature.