Journal
ULTRASONICS
Volume 137, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ultras.2023.107200
Keywords
Sparse array; Limited view; Guided wave imaging; Deep learning
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This paper proposes an end-to-end ultrasonic guided wave joint learning imaging method for sparse and limited-view transducer arrays, which significantly improves the quality of imaging results by integrating sparse feature reconstruction and deep learning imaging methods.
Sparse and limited-view ultrasonic guided wave imaging has become a research hotspot in the field. Studies have shown that traditional under-sampling ultrasonic imaging methods either require a significant amount of time to recover the full data or produce poor quality imaging results. To address these issues, this paper proposes an end-to-end ultrasonic guided wave joint learning imaging method for sparse and limited-view transducer arrays, which integrates sparse feature reconstruction and deep learning imaging methods. Numerical and experimental studies demonstrate that this approach significantly improves the quality of imaging results. The quality of imaging results for sparse and limited-view transducer arrays is evaluated and quantified using average corre-lation coefficients on the testing set. The feasibility and effectiveness of the proposed method have been verified.
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