Full-wavefield ultrasound imaging is an inspection methodology which provides accurate diagnosis of defects in structures. However, full-wavefield acquisition and processing is often a time consuming process. To tackle this problem, Compressive Sensing (CS) reconstruction algorithms have been proposed to speed up the acquisition process. Such reconstruction can be combined with Super-resolution Convolutional Neural Networks (SRCNN) schemes to obtain high-resolution images from low-resolution wavefield images. This paper proposes the combination of CS and SRCNN to recover images captured with a Scanning Laser Doppler Vibrometer (SLDV), by training the SRCNN with a dataset of ultrasound wave propagation images captured on typical aerospace structures using the SLDV. For the training of the SRCNN, a dataset of 300 structural full-wavefield images were captured on four different aerospace panels and various simulated defects. CS sub-sampling procedures were used to generate a dataset of simulated low-resolution image acquisition. The SRCNN was then trained to recover the original high-resolution images from the low-resolution images obtained after CS sub-sampling. The results demonstrate the capability of the technique for enhancing image resolution and quality while acquiring just 20% of the original number of scan points.
Ultrasonic guided wave dataset for super-resolution reconstruction of images from compressed wavefield acquisition / Esfandabadi Y.K.; Bilodeau M.; Masson P.; De Marchi L.. - ELETTRONICO. - 2:(2019), pp. 1846-1851. (Intervento presentato al convegno 12th International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT), IWSHM 2019 tenutosi a Stanford University, USA nel 2019) [10.12783/shm2019/32313].
Ultrasonic guided wave dataset for super-resolution reconstruction of images from compressed wavefield acquisition
De Marchi L.
2019
Abstract
Full-wavefield ultrasound imaging is an inspection methodology which provides accurate diagnosis of defects in structures. However, full-wavefield acquisition and processing is often a time consuming process. To tackle this problem, Compressive Sensing (CS) reconstruction algorithms have been proposed to speed up the acquisition process. Such reconstruction can be combined with Super-resolution Convolutional Neural Networks (SRCNN) schemes to obtain high-resolution images from low-resolution wavefield images. This paper proposes the combination of CS and SRCNN to recover images captured with a Scanning Laser Doppler Vibrometer (SLDV), by training the SRCNN with a dataset of ultrasound wave propagation images captured on typical aerospace structures using the SLDV. For the training of the SRCNN, a dataset of 300 structural full-wavefield images were captured on four different aerospace panels and various simulated defects. CS sub-sampling procedures were used to generate a dataset of simulated low-resolution image acquisition. The SRCNN was then trained to recover the original high-resolution images from the low-resolution images obtained after CS sub-sampling. The results demonstrate the capability of the technique for enhancing image resolution and quality while acquiring just 20% of the original number of scan points.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.