An efficient detection algorithm based on anisotropic diffusion for low-contrast defect

In this paper, we propose an efficient algorithm based on the anisotropic diffusion model to detect defect in a low-contrast surface image, especially aimed at anti-reflective (AR) glass. The proposed algorithm has two important procedures: (1) a modified anisotropic diffusion model and (2) morphological directivity filter. The modified diffusion model based on an adaptive edge threshold, the standard score (Z-score), is proposed to quickly and efficiently enhance the low-contrast defects. It acts as the adaptive enhancement process. The pixels with both the low gradient and the high Z-score or the high gradient and the low Z-score will generate a high diffusion coefficient. It enhances the gray levels of suspected defective edges and also preserves the original gray levels of an internal area of suspected defects, as well as generates the slight smoothing process for the noisy background. A simple and efficient method can easily segment the defects, followed by the effective morphological directivity filter, which removes noise from the thresholding image. The proposed algorithm is evaluated by a set of 23 low-contrast surface images of AR glass in this study. The experimental results show that the proposed algorithm is superior to the four competitive approaches. The defect detection results demonstrate that the proposed algorithm can segment the complete defects. In addition, the computational advantage compared to the Perona and Malik model, C-T model 1, C-T model 2, and histogram statistics model are about 4.48, 2.26, 19.53, and 26.63 times, respectively. It can be concluded that the proposed algorithm not only provides reliable inspection results but also improves the inspection efficiency over 2 times.