# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 1 Introduction
[PERSON] and [PERSON] [8] first proposed a learning-based super-resolution method and proved that the results of image reconstruction are significantly better than reconstruction-based algorithms. [PERSON] et al. [9] constructed a Markov Random Field to learn the correlation between HR and LR images. [PERSON] et al. [10] learn the sparse representation of images based on the principle of compressed sensing and prove the effectiveness of sparsity as prior knowledge for super-resolution problems. [PERSON] et al. [11] used a method based on compressed sensing to construct a model for super-resolution nondestructive detection imaging of uncooled infrared sensors. It generates super-resolution infrared phase images by learning sparse representations of LR images and sparse dictionaries in HR images. [PERSON] et al. [12] proposed a method based on super-resolution photoactivated infrared imaging for the measurement of spatially resolved thermal conductivity. Deep learning is a computing method that has developed rapidly in recent years. It learns the mapping relationship between input and target values through various network structures. With its powerful fitting ability, deep learning has achieved great success in various applications, such as equipment fault diagnosis [13; 14] and ground-penetrating radar image enhancement [15]. [PERSON] et al. [16] summarized the application status and future development trend of deep learning in the field of nondestructive testing. Image super-resolution is an important application direction of deep learning and it is also developing rapidly. SRCNN [17] is the first deep learning-based single-image super-resolution model. EDSR [18] introduces the ResNet [19] structure into the model to reduce the risk of model overfitting. The model has shown a good level in the field of image super-resolution imaging. The DBPN [20] model uses up- and down-sampling layers to extract the relationship between LR and HR and achieves good results. MZSR [21] combines zero-shot learning and meta-transformation learning to reduce the inference time of the test. With the proposal of the Transformer model [22], corresponding super-resolution models have also emerged in large numbers, such as RCAN [23], ENLCA [24], etc. Super-resolution technology based on deep learning has also made good progress in the field of nondestructive testing. [PERSON] et al. [25] proposed a new super-resolution method for detecting small subwavelength defects. The model uses two networks. The first network is to determine the location of the defect area and the second network is to detect the subwavelength details of the defect. [PERSON] et al. [26] proposed a defect-aware generative adversarial network model for super-resolution and defect detection of images after thermal infrared imaging, which improved the visibility of defects and the accuracy of detection. [PERSON] et al. [27] constructed a visual geometric group-UNet (VGG-UNet) model to optimize the ultrasound imaging image and the model can improve the resolution of the reconstructed ultrasound image. [PERSON] et al. [28] proposed a multi-layer deep learning network for super-resolution reconstruction of phased array ultrasonic images. The ultrasonic images reconstructed by the network have improved the calculation accuracy of defect center distance and defect area calculation accuracy. Although super-resolution technology based on deep learning has achieved good results in the field of nondestructive testing, to the authors' best knowledge, super-resolution imaging based on deep learning has not been studied in the field of laser ultrasonic nondestructive testing. In view of the above conclusions, we propose a deep learning-based ultrasound image super-resolution model for data augmentation of laser ultrasound images. It is a general image data enhancement model that can be applied to various image super-resolution applications. Specifically, our proposed image super-resolution model is a novel method for hierarchical, multi-scale feature learning.