本文選題：TFT-LCD + B樣條曲面擬合��； 參考：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：薄膜晶體管液晶顯示器(TFT-LCD)在技術(shù)和生產(chǎn)方面發(fā)展迅速,正逐漸往輕薄化、大尺寸化、高分辨率化發(fā)展。在大尺寸和大規(guī)模生產(chǎn)的同時,LCD面板出現(xiàn)各類缺陷的幾率也大大增加,不僅限制了產(chǎn)量,還提高了成本。針對TFT-LCD面板上Mura缺陷對比度低、邊緣模糊、形狀不規(guī)則的特點,在對大量檢測方法的嘗試后,論文重點研究并設(shè)計了一種在背景亮度不均勻條件下的TFT-LCD Mura缺陷檢測技術(shù)。根據(jù)實際的檢測需要,確立了高斯濾波去噪聲、雙三次B樣條曲面擬合去除背景、最大類間方差(Otsu)的雙γ分段指數(shù)變換對比度增強、Otsu法閾值分割缺陷和SEMU標(biāo)準(zhǔn)量化評價的檢測流程,對檢測過程中各階段關(guān)鍵技術(shù)進行了分析和推導(dǎo),并用Matlab軟件編譯實現(xiàn)。主要對以下幾點進行了研究:(1)首先分析總結(jié)了Mura的定義、產(chǎn)生的原因和分類,然后歸納了圖像噪聲的來源和常用的四種去噪方法。(2)在曲面擬合去除背景部分,先總結(jié)了二元三次多項式曲面擬合和雙三次B樣條曲面擬合的理論知識,采用乘積型方程反算雙三次B樣條曲面并對擬合數(shù)據(jù)進行壓縮以提高算法的速度和計算效率;針對傳統(tǒng)的雙三次B樣條擬合精度過高的問題,引入光順項對擬合曲面進行光順平滑,以減小Mura區(qū)域點對擬合曲面局部形狀的干擾。(3)介紹了兩種已有的對比度增強方法并分析其缺點,從而提出一種新的增強方法:通過引入Otsu法自動選取閾值作為分段函數(shù)的分段點;引入γ指數(shù)變換,對背景和目標(biāo)區(qū)域分別做不同的γ變換。所提方法能實現(xiàn)增強目標(biāo)區(qū)域?qū)Ρ榷炔⒁种票尘皡^(qū)域灰度值變化,同時增強了Mura缺陷的邊緣。再采用Otsu法閾值分割將圖像二值化,并提取缺陷面積等相關(guān)參數(shù)。(4)引入SEMU標(biāo)準(zhǔn),將前幾階段中提取的相關(guān)參數(shù)帶入公式,求得用于評級的Semu值,以判定算法的準(zhǔn)確性。將對比度增強和缺陷分割結(jié)合起來用以展示各算法的效果,證明了所提算法的實用性和高效性。通過Mura缺陷檢測實驗,總結(jié)歸納出檢測算法流程。實驗表明,本文所提檢測算法對各類常見的Mura缺陷均能有效檢出;對100個樣本圖像,在相關(guān)參數(shù)的設(shè)定下,有效檢出率達99%。
[Abstract]:TFT-LCD (thin Film Transistor liquid Crystal display) is developing rapidly in technology and production, and is developing to thinning, large scale and high resolution. At the same time as large size and mass production, LCD panels have increased the probability of various defects, not only limiting production, but also increasing the cost. In view of the characteristics of low contrast, fuzzy edge and irregular shape of Mura defects on TFT-LCD panel, this paper focuses on the research and design of a TFT-LCD Mura defect detection technology under the condition of uneven background brightness. According to the actual needs of detection, Gao Si filter noise removal, double cubic B-spline surface fitting to remove the background, The detection flow of threshold segmentation defect and SEMU standard quantitative evaluation of double 緯 -segment exponential transformation of maximum inter-class variance is analyzed and deduced. The key techniques in each stage of the detection are analyzed and deduced, and implemented by Matlab software. Firstly, the definition, causes and classification of Mura are analyzed and summarized, then the source of image noise and four common denoising methods. Firstly, the theoretical knowledge of biquadratic cubic polynomial surface fitting and bicubic B-spline surface fitting is summarized. The product type equation is used to inverse calculate the bicubic B-spline surface and the fitting data are compressed to improve the speed and computational efficiency of the algorithm. Aiming at the problem of high precision of traditional bicubic B-spline fitting, the fairing term is introduced to smooth the fitting surface. Two existing contrast enhancement methods are introduced and their shortcomings are analyzed in order to reduce the interference of Mura region points to the local shape of fitting surface. A new enhancement method is proposed: the threshold value is automatically selected as the segmentation point of the piecewise function by introducing the Otsu method, and the 緯 -exponential transformation is introduced to make different 緯 transformations for the background and the target region respectively. The proposed method can enhance the contrast of the target area and suppress the change of the gray value of the background area, and enhance the edge of the Mura defect at the same time. Then the binarization of the image is made by using Otsu threshold segmentation method, and the relevant parameters such as defect area are extracted into the SEMU standard. The relevant parameters extracted in the previous stages are brought into the formula to obtain the Semu value used for rating, so as to determine the accuracy of the algorithm. Contrast enhancement and defect segmentation are combined to show the effectiveness of each algorithm, which proves the practicability and efficiency of the proposed algorithm. Through the Mura defect detection experiment, summed up the detection algorithm flow. The experimental results show that the proposed detection algorithm can effectively detect all kinds of common Mura defects, and the effective detection rate of 100 sample images can reach 99.9% under the setting of related parameters.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN873.93;TP391.41

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