Ultrasound image super-resolution reconstruction based on semi-supervised CycleGAN

In ultrasonic testing, diffraction artifacts generated around defects increase the challenge of quantitatively characterizing defects. In this paper, we propose a label-enhanced semi-supervised CycleGAN network model, referred to as LESS-CycleGAN, which is a conditional cycle generative adversarial network designed for accurately characterizing defect morphology in ultrasonic testing images. The proposed method introduces paired cross-domain image samples during model training to achieve a defect transformation between the ultrasound image domain and the morphology image domain, thereby eliminating artifacts. Furthermore, the method incorporates a novel authenticity loss function to ensure high-precision defect reconstruction capability. To validate the effectiveness and robustness of the model, we use simulated 2D images of defects and corresponding ultrasonic detection images as training and test sets, and an actual ultrasonic phased array image of a test block as the validation set to evaluate the model's application performance. The experimental results demonstrate that the proposed method is convenient and effective, achieving subwavelength-scale defect reconstruction with good robustness.