本文選題：TFT-LCD + 陷波濾波��； 參考：《湖南理工學院》2017年碩士論文

【摘要】：液晶屏的制造過程極其復雜,需要近百道工序,在制造的過程中難免會出現許多類型的缺陷。因此對液晶屏缺陷的檢測在生產過程起著關鍵性作用。利用機器視覺技術實現液晶屏點燈缺陷的高速自動檢測是液晶屏自動化生產的重要研究課題之一。論文通過分析液晶屏點燈圖像中存在的缺陷和瑕疵的特征,提出基于圖像處理的快速、有效的缺陷檢測方法。論文主要進行了以下幾方面的研究:子像素級缺陷檢測方法研究:傳統(tǒng)的圖像學處理方法很難檢測出子像素級缺陷。我們通過研究液晶屏圖像的特點,發(fā)現同一型號液晶屏中像素均是規(guī)則排列,因此對缺陷圖像經過傅里葉變換能得到一致的頻譜圖,針對此特點,論文提出了一種基于陷波濾波和圖像配準的子像素缺陷檢測方法。首先用無缺陷模板圖像建立配準模板和陷波濾波模板;然后用配準模板對缺陷圖像進行圖像配準,解決屏幕偏移問題;再用陷波濾波模板進行濾波處理,濾除背景紋理,使缺陷更加明顯;最后對圖像閾值分割,找出缺陷。結果顯示,該方法可以準確、快速的檢測子像素級缺陷。中小型缺陷檢測方法研究:由于相機拍攝的液晶屏圖像缺陷、背景、細節(jié)、噪聲等信息都包含在一個較窄的灰度范圍內,導致缺陷與其他信息難以區(qū)分,因此,論文提出了一種融合局部熵與局部均勻度液晶屏缺陷檢測方法。將像素分布的局部熵值和局部均勻度值相結合,濾除空間鄰域內噪聲并獲得像素的空間分布,利用空間特征實現對中小型缺陷的檢測。結果顯示,論文方法不需要進行頻域處理便可準確找到缺陷,檢測速率較高,相比于其他算法,有較強魯棒性。Mura缺陷檢測方法研究:液晶屏Mura缺陷均具有背景整體亮度不均、灰度變化不明顯等特點,用基于機器視覺的方法從中檢測出缺陷是非常困難的。論文提出一種新的Mura缺陷檢測方法,首先對液晶屏圖像中背景與Mura缺陷灰度值差異進行分析,然后采用均值濾波和背景差分法來抑制背景雜波,再利用灰度約束獲取疑似Mura缺陷區(qū)域,最后提取疑似區(qū)域的灰度特征放入訓練后的BP神經網絡提取缺陷目標。結果顯示,論文方法有較好的除噪效果,檢測率較高。
[Abstract]:The manufacturing process of LCD screen is extremely complex and requires nearly 100 processes. Many kinds of defects will inevitably appear in the manufacturing process. Therefore, the detection of LCD screen defects plays a key role in the production process. It is one of the important research topics in the automatic production of LCD screen to realize high speed automatic detection of the defects of LCD screen with machine vision technology. Based on the analysis of defects and defects in LCD screen lamp images, a fast and effective defect detection method based on image processing is proposed in this paper. This paper mainly studies the following aspects: subpixel level defect detection method: traditional image processing methods are difficult to detect sub-pixel level defects. By studying the characteristics of the LCD screen image, we find that the pixels in the same LCD screen are arranged regularly, so we can get the consistent spectrum of the defective image by Fourier transform. In this paper, a subpixel defect detection method based on notch filtering and image registration is proposed. First, the registration template and notch filter template are established by using the non-defect template image; then, the defect image is registered with the registration template to solve the screen offset problem; and then the notch filter template is used to filter the background texture. Make the defect more obvious; finally, the image threshold segmentation, find out the defect. The results show that the method can detect subpixel defects accurately and quickly. Research on small and medium defect Detection methods: because the defects, background, details, noise and other information of LCD screen images taken by camera are all contained in a narrow gray range, it is difficult to distinguish the defects from other information. In this paper, a method for defect detection of liquid crystal screen with fusion of local entropy and local uniformity is proposed. The local entropy value and local uniformity value of pixel distribution are combined to filter the noise in the spatial neighborhood and to obtain the spatial distribution of the pixels. The detection of small and medium-sized defects is realized by using spatial features. The results show that the method can accurately find the defects without frequency domain processing, and the detection rate is higher. Compared with other algorithms, the method of robust. Mura defect detection method: the Mura defects of LCD screen have the uneven brightness of the background as a whole. It is very difficult to detect defects by machine vision based method because the gray level change is not obvious. In this paper, a new Mura defect detection method is proposed. Firstly, the difference between background and Mura defect gray value in LCD screen image is analyzed, and then the mean filter and background difference method are used to suppress background clutter. Then the suspected Mura defect region is obtained by using gray constraints, and the gray feature of the suspected area is finally extracted into the trained BP neural network to extract the defect target. The results show that the method has better denoising effect and high detection rate.
【學位授予單位】：湖南理工學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN873.93;TP391.41

【參考文獻】

相關期刊論文 前10條

1 王平;項志偉;胡進;喬非;;我國平板顯示行業(yè)發(fā)展現狀及發(fā)展趨勢分析[J];改革與開放;2016年21期

2 陳凌海;姚劍敏;郭太良;;基于改進Chan-Vese模型的液晶顯示屏Mura缺陷分割[J];液晶與顯示;2016年06期

3 張騰達;盧榮勝;張書真;;基于二維DFT的TFT-LCD平板表面缺陷檢測[J];光電工程;2016年03期

4 李力;王耀南;陳鐵健;;大尺寸LCD玻璃基板多視覺缺陷檢測系統(tǒng)研究[J];控制工程;2016年02期

5 竇兆玉;張奇志;周亞麗;劉俊;;基于機器視覺的液晶屏幕壞點檢測[J];北京信息科技大學學報(自然科學版);2015年05期

6 簡川霞;;TFT-LCD表面缺陷檢測方法綜述[J];電視技術;2015年09期

7 毛羽忻;楊俊強;曲勁松;吳珍榮;;基于局部熵的點目標檢測算法分析[J];火炮發(fā)射與控制學報;2014年03期

8 楊斯涵;;基于自適應尺度的小目標檢測方法[J];光電工程;2014年04期

9 盧小鵬;李輝;劉云杰;梁平;李坤;;基于Chan-Vese模型的TFT-LCD Mura缺陷快速分割算法[J];液晶與顯示;2014年01期

10 李坤;李輝;劉云杰;梁平;盧小鵬;;LCD Mura缺陷的B樣條曲面擬合背景抑制[J];光電工程;2014年02期

相關博士學位論文 前1條

1 張昱;基于機器視覺的TFT-LCD屏mura缺陷檢測技術研究[D];哈爾濱工業(yè)大學;2006年

相關碩士學位論文 前7條

1 李傳樂;TFT-LCD缺陷檢測系統(tǒng)中的圖像處理[D];吉林大學;2016年

2 謝瑞;TFT-LCD Mura缺陷自動檢測方法研究[D];合肥工業(yè)大學;2016年

3 李茂;基于機器視覺的TFT-LCD屏Mura缺陷檢測方法研究[D];電子科技大學;2013年

4 鄒園園;THz圖像條紋噪聲消除方法研究[D];首都師范大學;2009年

5 黃麗;BP神經網絡算法改進及應用研究[D];重慶師范大學;2008年

6 何志廣;基于圖像處理技術的TFT-LCD微米級缺陷檢測方法研究[D];哈爾濱工業(yè)大學;2007年

7 彭紹湖;TFT-LCD液晶顯示器表面缺陷檢測技術研究[D];廣東工業(yè)大學;2007年

，

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