Super-resolution visualization of subwavelength defects via deep learning-enhanced ultrasonic beamforming: A proof-of-principle study

Detecting small, subwavelength defect has known to be a challenging task mainly due to the diffraction limit, according to which the minimum resolvable size is in the order of the wavelength of a propagating wave. In this proof-of-concept study, we present a deep learning-enhanced super-resolution ultrasonic beamforming approach that computationally exceeds the diffraction limit and visualizes subwavelength defects. The proposed super-resolution approach is a novel subwavelength beamforming methodology enabled by a hierarchical deep neural network architecture. The first network (the detection network) globally detects defective regions from an ultrasonic beamforming image. Subsequently, the second network (the super-resolution network) locally resolves subwavelength-scale fine details of the detected defects. We validate the proposed approach using two independent datasets: a bulk wave array dataset generated by numerical simulations and guided wave array dataset generated by laboratory experiments. The results demonstrate that our deep learning super-resolution ultrasonic beamforming approach not only enables visualization of fine structural features of subwavelength defects, but also outperforms the existing widely-accepted super-resolution algorithm (time-reversal MUSIC). We also study key factors of the performance of our approach and discuss its applicability and limitations.