An anisotropic diffusion-based defect detection for low-contrast glass substrates

Abstract

In this paper, we propose an anisotropic diffusion scheme to detect defects in low-contrast surface images and, especially, aim at glass substrates used in TFT-LCDs (Thin Film Transistor-Liquid Crystal Displays). In a sensed image of glass substrate, the gray levels of defects and background are hardly distinguishable and result in a low-contrast image. Therefore, thresholding and edge detection techniques cannot be applied to detect subtle defects in the glass substrates surface. Although the traditional diffusion model can effectively smooth noise and irregularity of a faultless background in an image, it can only passively stop the diffusion process to preserve the original low-contrast gray values of defect edges. The proposed diffusion method in this paper can simultaneously carry out the smoothing and sharpening operations so that a simple thresholding can be used to segment the intensified defects in the resulting image. The method adaptively triggers the smoothing process in faultless areas to make the background uniform, and performs the sharpening process in defective areas to enhance anomalies. Experimental results from a number of glass substrate samples including backlight panels and LCD glass substrates have shown the efficacy of the proposed diffusion scheme in low-contrast surface inspection.