A Novel Defect Inspection Method for the TFT-LCD Image Based on ...

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Wavelet Trans<strong>for</strong>m domain research has <strong>the</strong> best results in case of precise noise modeling [6][7]. There is a problem in <strong>the</strong> random noise modeling because <strong>the</strong> random noise is expressed by camera noise, camera distortion and random noise, etc. And <strong>the</strong> o<strong>the</strong>r method to help Mura segmentation is <strong>the</strong> enlargement of Mura signal. If <strong>the</strong> frequency band of Mura signal could be found exactly, we could emphasize it by magnifying this band. Generally, because <strong>the</strong> signal affects all frequency spectrums, finding precise cut-off frequency of <strong>the</strong> Mura signal is impossible. But from <strong>the</strong> human visual system point of view, we can emphasize more visible frequency given by <strong>the</strong> CSF experiment [8-10]. One of major HVS characteristics is that <strong>the</strong> HVS has different frequency sensitivity. Adapted CSF is proposed to visualize more salient object such as Muras. In this paper, <strong>the</strong> <strong>TFT</strong>-<strong>LCD</strong> image defect detection algorithm based on HVS is proposed. The PR is used <strong>for</strong> background signal flattening while <strong>the</strong> proposed ACSF are used to enlarge Mura signal. Then tri-modal thresholding, which determines defect candidate as out of mean value, is employed <strong>for</strong> Mura segmentation. Finally, false positive defects are eliminated based on HVS. 2 The Characteristics of <strong>TFT</strong>-<strong>LCD</strong> <strong>Image</strong> Inherently <strong>TFT</strong>-<strong>LCD</strong> panel is non-light emitting display devices, thus <strong>the</strong>y need an external light source called BLU. There have been many ef<strong>for</strong>ts to make BLU as ideal plane light sources so that <strong>the</strong> illumination from <strong>the</strong> frontal panel can be uni<strong>for</strong>m [7]. Because <strong>the</strong> BLU is placed at <strong>the</strong> panel border, <strong>the</strong> light from <strong>the</strong> source would decrease according to <strong>the</strong> distance. To make <strong>the</strong> light more uni<strong>for</strong>mly distributed several additional sheets such as diffuser sheet, horizontal and vertical prism sheets are required. Through <strong>the</strong>se sheets a light can be diffused and be concentrated. Examples of a BLU structure and highlighted non-uni<strong>for</strong>m light transmission from <strong>the</strong> source are shown in Fig. 2. (a) Gray Level (b) Fig. 2. The structure of a BLU and light transmission from <strong>the</strong> light source (a) The BLU structure (b) light emitting from a light source. (a) (b) Line Profiles 200 Line 183 Line 190 180 170 160 150 140 130 120 110 100 90 0 50 100 150 200 250 Position (c) Fig.3. Sample <strong>TFT</strong>-<strong>LCD</strong> image with Mura defects and its characteristics. (a) The <strong>TFT</strong>-<strong>LCD</strong> image with Mura defects, (b) signal generated <strong>TFT</strong>-<strong>LCD</strong> image having Muras of various size and strength and (c) line profiles of dotted circle and arrow.
The light is diffused and concentrated through <strong>the</strong>se sheets. But <strong>the</strong>re still exist some illumination variations measured by LU of more than 80%. Above 80% LU is considered acceptable in industry. The luminance values <strong>for</strong> calculating <strong>the</strong> LU are acquired from <strong>the</strong> predetermined positions. Mura defects are darker or brighter than <strong>the</strong>ir neighboring normal regions in <strong>TFT</strong>-<strong>LCD</strong> images. They have various shapes and strengths according to <strong>the</strong> cause of generation. Some of <strong>the</strong>m occur when unexpected <strong>for</strong>eign materials are inserted into <strong>the</strong> layers in a display panel and <strong>the</strong>y can be generated due to non-uni<strong>for</strong>m liquid crystal distribution, air insertion, and partial press by external <strong>for</strong>ces, etc [8]. Because of <strong>the</strong> non-uni<strong>for</strong>m illumination characteristic of <strong>the</strong> background signal, dark Mura can be brighter than <strong>the</strong> normal background region. Two of our experimental <strong>TFT</strong>- <strong>LCD</strong> panel images are shown in Fig. 3(a), (b). Fig. 3(a) includes 4 White and Dark Mura defects and Fig. 3(b) has <strong>the</strong> signal generated Muras with various size and strength. The position of Muras are encircled or marked with arrows. The line profile of dotted circle and arrow is shown in Fig.3 (c). From <strong>the</strong>se figures, <strong>the</strong> illumination of <strong>TFT</strong>-<strong>LCD</strong> image shows local and global variations at <strong>the</strong> same time. 3 Proposed <strong>Defect</strong> Detection Algorithm No Not Mura Input <strong>Image</strong> Discrete Cosine Trans<strong>for</strong>m Adapted CSF Filtering Inverse Discrete Cosine Trans<strong>for</strong>m Polynomial Regression Analysis Tri-modal Thresholding Labeling L if C L Result <strong>Image</strong> Mura Fig.4. The proposed block diagram <strong>for</strong> <strong>TFT</strong>-<strong>LCD</strong> image defect detection Yes The flow chart of <strong>the</strong> proposed algorithm is seen in Fig. 4. First, <strong>the</strong> input image is DCT trans<strong>for</strong>med and <strong>the</strong>n proposed ACSF filter is applied to enlarge <strong>the</strong> Mura signal. After <strong>the</strong> signal is inverse Discrete Cosine Trans<strong>for</strong>med, polynomial regression (PR) process is employed <strong>for</strong> flattening. Then <strong>the</strong> enhanced image is segmented by trimodal thresholding to find White and Dark Mura at once. To leave out reliable defects, false defects are eliminated if <strong>the</strong> mean gray level of a defect does not have much difference from <strong>the</strong>ir background. 3.1 Adapted CSF in DCT Domain The human visual system(HVS) has different frequency sensitivity and that is ma<strong>the</strong>matically represented as CSF given by <strong>the</strong> experiment[8][9][10]. The HVS can detect more easily <strong>the</strong> difference varying in low frequency than high frequency. The CSF can be expressed by a function and CSF graph is shown in Fig. 5. Where, 1. 1 H( f ) 2. 6( 0. 192 0. 114 f ) exp( ( 0. 114 f ) ) (2) f f f is <strong>the</strong> frequency having unit of 2 x 2 y cycle/degree. HVS have best sensitivity around 8[cycles/degree] and above 40[cycles/degree] its value become smaller and smaller. CSF Value 1.4 1.2 1 0.8 0.6 0.4 0.2 0 CSF Graph 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 Frequency[Cycle/Degree] Fig.5. The contrast sensitivity function graph. There have been many ef<strong>for</strong>ts to use CSF to <strong>the</strong>ir applications, especially in compression. In order to use <strong>the</strong> CSF in real application relative sensitivity value is important[10]. Without loss of generality, <strong>the</strong> maximum CSF frequency can be normalized as 2 in DCT domain. The DCT has good energy compaction property and it has also fast algorithm. The DCT has no phase in<strong>for</strong>mation, it only consider <strong>the</strong> magnitude of trans<strong>for</strong>med data. The CSF has only magnitude, so it will be enough to consider <strong>the</strong> proposed ACSF in DCT domain. The proposed adapted CSF in DCT is ma<strong>the</strong>matically expressed by <strong>the</strong> followings.
Page 1: A Novel De
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A Novel Defect Inspection Method for the TFT-LCD Image Based on ...

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