Image enhancement model for ultrasonic nondestructive testing based on deep learning super-resolution

Jinxuan Zhu; Xinfang Zhang; Ruixian He; Luo Kang; Guoyou Wang; Yan Huang

doi:10.1117/12.2691203

7 March 2024 Image enhancement model for ultrasonic nondestructive testing based on deep learning super-resolution

Jinxuan Zhu, Xinfang Zhang, Ruixian He, Luo Kang, Guoyou Wang, Yan Huang

Proceedings Volume 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 130880F (2024) https://doi.org/10.1117/12.2691203
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Ultrasonic nondestructive testing has been widely used in industry due to its various advantages. However. with the increasing demand for non-destructive testing accuracy, low-resolution ultrasonic images can easily lead to misidentification of defects. How to improve the resolution of ultrasonic images has become a key issue restricting development of the non-destructive testing industry. Therefore. this paper proposes a super-resolution model for ultrasonic NDT images consisting of up and down sampling layers and deep residual networks. The model learns the degraded 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 to complete the super-resolution task of the image. Experimental results on ultrasound image datasets also validate the effectiveness of the model, outperforming other models in peak signa-to-noise ratio (PSNR) and structural similarity (SSIM) metrics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinxuan Zhu, Xinfang Zhang, Ruixian He, Luo Kang, Guoyou Wang, and Yan Huang "Image enhancement model for ultrasonic nondestructive testing based on deep learning super-resolution", Proc. SPIE 13088, MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 130880F (7 March 2024); https://doi.org/10.1117/12.2691203

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KEYWORDS

Nondestructive evaluation

Ultrasonics

Super resolution

Deep learning

Image enhancement

Data modeling

Industry

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