An Accurate Mura Defect Vision Inspection Method Using Outlier-Prejudging-Based Image Background Construction and Region-Gradient-Based Level Set

The visual inspection of Mura defects is still a challenging task in the quality control of panel displays because of the intrinsically nonuniform brightness and blurry contours of these defects. The current methods cannot detect all Mura defect types simultaneously, especially small defects. In this paper, we introduce an accurate Mura defect visual inspection (AMVI) method for the fast simultaneous inspection of various Mura defect types. The method consists of two parts: an outlier-prejudging-based image background construction (OPBC) algorithm is proposed to quickly reduce the influence of image backgrounds with uneven brightness and to coarsely estimate the candidate regions of Mura defects. Then, a novel region-gradient-based level set (RGLS) algorithm is applied only to these candidate regions to quickly and accurately segment the contours of the Mura defects. To demonstrate the performance of AMVI, several experiments are conducted to compare AMVI with other popular visual inspection methods are conducted. The experimental results show that AMVI tends to achieve better inspection performance and can quickly and accurately inspect a greater number of Mura defect types, especially for small and large Mura defects with uneven backlight.