Ultrasonic array imaging of porosity defects with contrast enhancement based on dominant response subtraction

• Published in: Ultrasonics 2023-12, Vol.135, p.107109-107109, Article 107109
• Main Authors: Bai, Long, Guo, Changrong, Ye, Tao, Xu, Jianfeng
• Format: Article
• Language: English
• Subjects: Contrast enhancement; Convolutional sparse coding; Porosity defect; TFM imaging; Ultrasonic array
• ISSN: 0041-624X; 1874-9968
• DOI: 10.1016/j.ultras.2023.107109

Porosity defects can be found in many engineering structures and their inspection remains a challenge in the field of ultrasonic non-destructive testing. In this paper, ultrasonic array imaging of porosity defects has been studied with the aim of improving the image quality in the “dead zone”, which is caused by the masking effects of the uppermost pores. The proposed approach first extracts contributions of the uppermost pores based on a single scattering model by adopting convolutional sparse coding. The extracted dominant contributions are then subtracted from the array data before forming an image, facilitating detection and localization of pores in the shadow zone. The performance of the proposed approach has been studied in simulation and experiments, and the mean localization errors of the pores are small (i.e., within 0.27 mm or 0.14λ). In addition, the effects of measurement noise and imaging parameters on robustness of the imaging result have been analyzed, providing guidelines for practical implementation of the proposed approach. •Ultrasonic array imaging of porosity defects has been studied.•Dominant response signal was extracted using convolutional sparse coding.•Image contrast was improved by subtracting the dominant response from the array data.•The effects of measurement noise and imaging parameters have been analyzed.