Wuhan Jingce Electronic Group Co., Ltd.

Feng Yao

Yafei Shen

Biaohua Deng

Kai Chen

Fangbo Lu

The disclosure provides a background suppression method and a detecting device in automatic optical detection for a display panel, wherein the background suppression method includes the following steps: S: collecting a pure color image of the display panel; S: performing multi-level wavelet decomposition on the pure color image to obtain a series of high-frequency sub-bands and low-frequency sub-bands; S: performing coefficient smoothing process on high-frequency sub-bands in multiple directions of each level, and performing contrast enhancement process on each level of low-frequency sub-bands; S: the processed high-frequency sub-bands and the processed low-frequency sub-bands are subjected to wavelet reconstruction to obtain a defect image after background suppression. The disclosure performs multi-scale and multi-resolution decomposition on the image, and performs texture suppression and image enhancement on the decomposed high-frequency sub-bands and low-frequency sub-bands respectively, and can maintain the contrast of the original defect while suppressing the background texture.

Background suppression method and detecting device in automatic optical detection of display panel

The present disclosure discloses a GPU-based TFT-LCD Mura defect detection method, comprising: (1) establishing a studentized residual based double-second-order regression diagnosis model based original image data to obtain double-second-order regression background data; (2) obtaining influence quantities of respective data points on fitted values according to the original image data and the double-second-order regression background image data; (3) excluding outliers and influential points in the original image data according to the influence quantities to obtain a new pixel point set; (4) establishing a double-N-order polynomial surface fitting model according to the new pixel point set to obtain double-N-order background image data; (5) obtaining a residual image R according to the double-N-order background image data and the original image data, and performing threshold segmentation on the residual image to obtain a threshold segmentation image; and (6) performing morphological processing on the threshold segmentation image to obtain an eroded and dilated image, thereby achieving effective segmentation of Mura defects with uneven brightness distribution.

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Hubei, China

Feng Yao