A Terahertz Fast Imaging Method for Debonding Defects of Thermal Barrier Coatings Based on Dual-Channel Convolutional Neural Network

To tackle the inefficiency of terahertz (THz)-based C-scan defect detection for thermal barrier coatings (TBCs), a dual-channel convolutional neural network–based THz fast imaging method is proposed. In this paper, the finite-difference time-domain (FDTD) method is used to prepare the training set. In the numerical simulation, the actual C-scan step is simulated by grid division of different sizes. The large step THz image is preliminarily reconstructed by bicubic interpolation, and then the deep and shallow features in the image are extracted by the dual-channel convolution neural network and the image under small step is reconstructed by different weight refusion, so as to improve the detection efficiency by reducing the number of C-scan points. Gaussian white noise with different distributions is employed when simulating the real test image. The experimental results show that compared with bicubic, ICBI, SRCNN, and ResNet, the dual-channel convolutional neural network improves PSNR (peak signal-to-noise ratio) by 2.85, 2.81, 2.25, and 1.54, and improves by 0.019, 0.014, 0.014, and 0.009 on SSIM (structural similarity).