• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.604-608
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• 2008

The TFT-LCD image allows non-uniform illumination variation and that is one of main difficulties of finding defect region. The SQI (self quotient image) has the HPF (high pass filter) shape and is used to reduce low frequency-lightness component. In this paper, we proposed the TFT-LCD defect-enhancement algorithm using characteristics of the SQI, that is the SQI has low-frequency flattening effect and maintains local variation. The proposed method has superior flattening effect and defect-enhancement effect compared with previous the TFT-LCD image preprocessing.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1288-1296
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• 2016

The reliable detection of the limited defect in TFT-LCD images is difficult due to the small intensity difference with the background. However, the proposed detection method reliably detects the limited defect by enhancing the TFT-LCD image based on the cumulative histogram and then detecting the defect through the mean and standard deviation of the enhanced image. Notably, an image enhancement using a cumulative histogram increases the intensity contrast between the background and the limited defect, which then allows defects to be detected by using the mean and standard deviation of the enhanced image. Furthermore, through the comparison with the histogram equalization, we confirm that the proposed algorithm suppresses the emphasis of the noise. Experimental comparative results using real TFT-LCD images and pseudo images show that the proposed method detects the limited defect more reliably than conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.20-27
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• 2007

Generally, the TFT-LCD image signal have nonuniform brightness and are composed of largely varying background signal, noise signal and abruptly changing Mura signal within Mura region. In this paper, Mura region enhancing algorithms using the proposed modified-MTF, which describes how human-visual-system's sensitivity varies in frequency domain, is proposed. The validity of the proposed algorithm was demonstrated ideal 1-dimensional signal and also then it was also tested TFT-LCD image. By the experimental results, the proposed algorithm is very effective in TFT-LCD image Mura enhancement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.734-737
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• 2008

This paper presents a new automatic TFT-LCD image quality optimization system. We also have developed new algorithms using 6-point programmable matching technique with reference gamma curve, and automatic power setting sequence. It optimizes automatically gamma adjustment and power setting registers in mobile TFT-LCD driver IC to reduce gamma correction error, adjusting time, and flicker. Developed algorithms and programs are generally applicable for most of the TFT-LCD modules. The proposed optimization system contains module-under-test (MUT, TFT-LCD module), control program, multimedia display tester for measuring luminance and flicker, and control board for interface between PC and TFT-LCD module. The control board is designed with DSP, and it supports various interfaces such as RGB and CPU. Developed automatic image quality optimization system showed significantly reduced gamma adjusting time, reduced flicker, and much less average gamma error than competing system. We believe that the proposed system is very useful to provide high image quality TFT-LCD and to reduce developing process time using optimized gamma-curve setting and automatic power setting.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.204-214
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• 2012

This paper proposes an image segmentation for a vision-based automated defect inspection system on surface image of TFT-LCD(Thin Film Transistor Liquid Crystal Display) panels. TFT-LCD images have non-uniform brightness, which is hard to finding defective regions. Although there are several methods or proposed algorithms, it is difficult to divide the defect with high reliability because of non-uniform properties in the image. Kamel and Zhao disclosed a method which based on logical stage algorithm for segmentation of graphics and character. This method is a one of the local segmentation method that has a advantage. It is that characters and graphics are well segmented in an image which has non-uniform property. As TFT-LCD panel image has a same property, so this paper proposes new algorithm to segment regions of defects based on Kamel and Zhao's algorithm. Our algorithm has an advantage that there are a few ghost objects around the defects. We had experiments to prove performance in real TFT-LCD panel images, and comparing with the FFT(Fast Fourier Transform) method which is used a bandpass filter.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.633-640
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• 2020

Defect region segmentation of TFT-LCD images is performed by combining defect pixels detected by a defect detection method into defect region, or by using morphological operations to segment defect region. Therefore, the result of segmentation of the defect region is highly dependent on the defect detection result. In this paper, we propose a method which segments defect regions sequentially according to the possibility of being included in defect regions in TFT-LCD images. The proposed method repeats the process of detecting a seed using the median value and the median absolute deviation of the image, and segments the defect region using the seeded region growing method. We confirmed the superiority of the proposed method to segment defect regions using pseudo-images and real TFT-LCD images.

• 전자공학회논문지 IE
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• v.45 no.2
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• pp.20-27
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• 2008

In this paper, we propose a new image processing technique that reliably detects the various defects of TFT-LCD polarizing films. The image of polarizing film is acquisited from reflected laser beam First, we apply the morphological image processing technique to remove the background noise. Next, we use the 2-dimensional LMS adaptive filtering and statistical characteristics to detect the white and black defects. Performance of the proposed method is evaluated on real TFT-LCD polarizing film samples.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.525-534
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• 2013

Recently, mura defect inspection techniques are receiving attention in LCD production procedure since demands of TFT-LCD are growing. In this paper, we propose an automatic mura defect inspection method using gabor wavelet transform and DCT. First, we generate a reference panel image using DCT based method. For original panel image and generated reference panel image, we apply a gabor wavelet transform to eliminate texture information in images. Then, we extract mura defect regions from the difference image between gabor wavelet transform image of original panel and generated reference panel image. Finally, all mura defect regions are quantified to detect accurate mura defects. Experimental results show that our method is more accurate and efficient than previous methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.167-169
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• 2010

This paper describes hardware design and FPGA verification of TFT-LCD controller used in mobile devices widely. TFT-LCD controller outputs pixel's color information red, green, blue and Hsync, Vsync synchronization signals. We used verilog-hdl to describe TFT-LCD controller and simulated it using modelsim software and verified it's exact operation on Xilinx FPGA. Framebuffer made up Block RAM form in FPGA and TFT-LCD displayed image file.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1115-1127
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• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.