Subsurface detail fusion for anomaly detection in non-stationary thermal wave imaging

摘要

Subsurface anomalies in real objects exhibit their signatures at different instances in post-processing details, depending on their depth and size. This unscrambled information may not explore all of the details in a single image to facilitate a whole-field subsurface analysis leading to a few details being missed in interpretation, which demands the consolidation of unscrambled details into a single image. This paper proposes a wavelet-based data fusion modality to embed all of the subsurface details into a single image to facilitate complete subsurface analysis. The proposed detection modality has been experimentally verified with a carbon fibre-reinforced plastic specimen using guadratic frequency-modulated thermal wave imaging (QFMTWI).