Tensor integrated total variation regularization for thermography NDT of composites

The carbon fiber reinforced polymer (CFRP) is being used frequently in the manufacturing of aerospace, rail, and other mechanical structures, where the optical pulse thermography (OPT)-based non-destructive testing (NDT) is generally used for the quality inspection, However, the output thermal sequences in OPT-based inspection suffer from uneven illumination and high-frequency thermal noise. Consequently, the inspection of defects (debonds) becomes difficult. To remedy it, post-image processing algorithms are generally carried out. The usefulness of such algorithms, however, is limited by the shape-complexity of the CFRP specimen. In this paper, we propose a tensor nuclear norm (TNN)-based low-rank and sparse total variation regularization (TVR) for CFRP debond defect detection. The integrated low-rank and sparse components are jointly and iteratively optimized. The proposed algorithm removes noise and segments/extracts the defects information from the thermal video se-quences with improved resolution and contrast. Compared to the general image processing algorithm used for OPT-based NDT testing, the proposed algorithm is faster and in terms of F-score is more accurate.

Automated summary

In this paper, a tensor nuclear norm-based low-rank and sparse total variation regularization method is proposed to address the issue of uneven illumination and high-frequency thermal noise in OPT-based inspection. The proposed algorithm removes noise, segments/extracts defect information from thermal video sequences, and improves resolution and contrast.