Abstract: In order to solve the problem of low accuracy caused by mixed Poisson-Gaussian noise in Terahertz (THz) images of aramid fiber honeycomb material in contour detection of defects, a THz image denoising model is constructed based on the Anscombe transformation and wavelet threshold method. The variance of Gaussian noise is a necessary parameter of denoising model, but the noise distribution of THz images is unknown. Meanwhile, the texture and noise are mixed in high frequency, which gives challenges to accurate variance estimation. Firstly, taking the texture geometry of the sample as the prior information, the Benzene-ring operator is constructed to remove the texture of the THz image, so that the high-frequency components only contain noise. Secondly, an improved Logistic chaotic mapping is proposed to improve the diversity of dataset to train Elman neural network for building the mapping relationship between the high-frequency component and the variance of Gaussian noise. Finally, the Anscombe transformation is performed to transform the mixed Poisson-Gaussian noise to gaussian noise. The THz denoised image is obtained by using wavelet threshold method and Anscombe inverse transformation. The experiment and simulation results show that the proposed model can eliminate the mixed Poisson-Gaussian noise better than other three methods and improves the accuracy of defect contour detections. Compared with the Gaussian filtering, wavelet threshold and non-local mean value methods, the average gradient index is increased by 12%, 33%, and 9%, and the absolute error of defect area is reduced by 234 mm², 304 mm², and 263 mm², respectively.

Key words: Terahertz nondestructive testing, mixed Poisson-Gaussian noise, Benzene-ring operator, noise estimation, image denoising