# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 1 Introduction  
Ultrasonic nondestructive testing has been widely used in industry due to its various advantages. However, with the increasing requirements for nondestructive testing accuracy, low-resolution ultrasonic images can easily lead to defect recognition errors. How to improve the resolution of ultrasonic images has become a key issue restricting the development of the nondestructive testing industry. Therefore, this paper proposes an ultrasonic nondestructive testing image super-resolution model consisting of up- and down-sampling layers and a deep residual network and uses the Charbonnier loss function to adjust the model performance. The model learns the degradation features of the image through the up- and down-sampling layers and learns the intrinsic features of the image through the deep residual network so as to learn the feature information of the image more completely. The Charbonnier loss function can better converge the model, thereby completing the image super-resolution task. The experimental results on the ultrasonic image data set also verify the effectiveness of the model, which is superior to other models in terms of peak symbol noise ratio (PSNR) and structural similarity index measure (SSIM) indicators. Laser ultrasonic technology is an emerging technology in the field of nondestructive testing. Its basic principle is to focus the laser on the surface of the material, so that the material generates heat, strain, and stress fields on the surface of the material, and thengenerates ultrasonic waves inside the object. Due to its advantages such as non-contact, no coupling agent, high precision, and no damage, it has been rapidly developed and applied in ultrasonic nondestructive testing. With the development of nondestructive testing, higher requirements are placed on the quality of ultrasonic images. High-resolution detection images have become the key to improving detection accuracy. However, due to the imaging mechanism of ultrasonic images, the resolution of ultrasonic images is often low, which brings great difficulties to the task of nondestructive testing. Therefore, improving the resolution of ultrasonic images will play a vital role in the development of nondestructive testing, which is also the key to promoting the development of nondestructive testing. Super-resolution imaging is a widely used computational imaging technology, which is used to explore and restore high-resolution (HR) images from low-resolution (LR) images in order to improve the accuracy of image detail description. High-resolution images will greatly increase the accuracy of nondestructive testing work. Research has shown that it is feasible to achieve super-resolution imaging [1] by enhancing the hardware to break through the diffraction limit. For example, hyperlens-based ultrasound transducers have been developed to achieve super-resolution imaging of ultrasound echoes. Hyperlens technology [2; 3; 4] has been proven to have great potential, but its application value in practical industrial nondestructive testing remains to be studied due to the complexity in the design and fabrication of metamaterial-based hyperlens. On the other hand, studies have shown that super-resolution enhancement of ultrasound imaging can be performed by computational methods, such as the multiple signal classification algorithm combined with time reversal, which is called TR-MUSIC [5; 6]. The advantage of TR-MUSIC is that it can be perfectly combined with the array information of phased array ultrasound, without modifying the phased array ultrasound system. [PERSON] et al. [7] discussed the application of two computational methods based on TR-MUSIC and its phase-coherent form (PC-MUSIC) in the super-resolution of nondestructive testing of solid processing. However, the TR-MUSIC method is easily affected by noise and its imaging effect is poor when the noise is large. This paper mainly focuses on learning-based super-resolution methods.