Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
Source abstract
Abstract
Ultrasonic nondestructive testing has been widely used in various industries due to its simple operation and harmlessness for the object to be detected. However, due to the mechanism of ultrasonic image generation, the generated ultrasonic images often have low resolution, which greatly affects the final detection results. How to improve the resolution of ultrasonic images has become the key to improving the accuracy of defect detection. Therefore, this paper proposes an ultrasonic super-resolution model based on up- and down-sampling layers and multi-layer residual networks combined with Charbonnier loss function. The degradation features of the image are learned through up- and down-sampling layers, and the intrinsic features of the image are learned through multi-layer residual networks, so that all the feature information of the image is fully learned. The Charbonnier loss function accelerates the convergence of the model. Experimental results show that the model proposed in this paper outperforms the common model performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Processing Techniques · Image and Signal Denoising Methods · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
School of Automation and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China
References (33)
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