Sparse Based Super Resolution Multilayer Ultrasonic Imaging

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 discuss different techniques such as greedy based algorithms as well as ℓ1-norm minimization based approach to solve the proposed sparse signal representation based method. Experimental validation on real data, gathered from a solid test sample immersed in water, demonstrates that sparse signal recovery based techniques outperform the conventional methods available in the literature.