A quick judgment method for split-screen Mura

Abstract

The invention belongs to the technical field of display. The invention discloses a quick judgment method for split screen Mura. The method comprises the following steps: selecting a strip-shaped areanear a center line of a to-be-detected image as a detection area, sliding in the detection area through a sliding window to obtain a gray mean value difference value, comparing the maximum value of the difference value with a set threshold value, if the maximum value of the difference value is greater than the threshold value, judging that the split screen Mura is abnormal, or else judging that the display screen is normal. According to the method, the split-screen Mura defect of the display screen can be simply and efficiently judged, and the detection time is shortened while the judgment accuracy requirement of a user is met.

Description

technical field [0001] The invention relates to the field of display technology, in particular to a method for quickly judging split-screen Mura. Background technique [0002] Color mottle defects (Mura) inevitably occur in the production process of flat panel displays. Demura equipment is a color spot defect repair system integrating high-precision professional signal generator, standard optical measuring instrument, automatic crimping and control adjustment software. In the stain repair system, TFT-LCD often has some abnormal defects, such as point abnormalities, foreign objects, line abnormalities, etc. The most common defect is the split screen Mura after crimping. [0003] If the split-screen Mura is missed, abnormal phenomena will appear on the display screen after direct repair, which will seriously affect the subsequent process. The Demura equipment system determines that the detection of split-screen Mura can only be performed on a specified grayscale image, so si...

AI Technical Summary

Problems solved by technology

For example, the gradient-based edge detection operator is a traditional edge detection method, which performs first-order differentiation on each pixel of the image. Commonly used are Roberts operator, Prewitt operator and Sobel operator; this method may lead to excessive detection. Many edge points make the resulting edge image rough

For example, the edge detection operator based on the second-order differential performs second-order differential on each pixel of the image, commonly used are Laplace operator, LOG operator and Canny operator; this method will blur the edge for noise-free images, so In use, a slightly larger filter scale is often selected, but it is easy to lose some edge details of the image.

For example, wavelet multi-scale edge detection uses the multi-scale characteristics of wavelet transform to obtain the multi-scale edge information of the image, and obtains the detailed edge information of the image by adjusting the scale of wavelet transform; the problem with this method is that the multi-scale edge detection operator The choice of and how to adaptively determine the best filter scale for edge detection

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