Research on the super-resolution reconstruction method of Lamb wave sparse TFM imaging

The sparse ultrasonic total focus method (TFM) encounters challenges such as Lamb wave mode conversion and diminished imaging resolution. In this paper, a sparse array solution method is proposed to simplify the imaging computation using sparse matrices. Furthermore, a nested U-Net network with a transformer architecture (TransNU-Net) was developed to address the limitations of conventional U-Net. These limitations include the absence of explicit modelling of distant connections and the lower representation capabilities of standard networks. The purpose of the TransNU-Net is to enhance the accuracy of sparse imaging results by enabling precise segmentation. This network architecture utilises a U-Net with different depths but common weights to effectively represent defects. It uses the transformer to recover local spatial information and improve the detection of small details. Experimental results show that the obtained sparse matrix reduces the imaging time by half while maintaining an effective aperture. Compared to mainstream U-Net variants, TransNU-Net exhibits a more lightweight network structure and superior performance. The imaging results demonstrate an improvement of 34.45% and 89.10% in defect centre spacing for defects with spacing greater than the resolution threshold, respectively. For defects with spacing less than the resolution threshold, this method achieves super-resolution reconstruction.