Local Sparseness and Image Fusion for Defect Inspection in Eddy Current Pulsed Thermography

Defect feature extraction and the analysis based on eddy current pulsed thermography (ECPT) technique are a research focus in non-destructive testing area. In this paper, a new feature extraction method based on thermography is proposed to enhance quantitative defect information. The proposed method included entropy-based image selection, local (element-wise) sparse and low-rank decomposition, and image fusion can increase the contrast of defect area and background and extract more useful defect features than other two common feature extraction algorithms in ECPT. The experiments including comparison results are provided to demonstrate the capabilities and benefits of the proposed algorithm. More meaningful defect information of the experimental specimens is reserved from raw ECPT data and background are suppressed severely compared with other feature extraction algorithms.