TFT-LCD mura defect detection using DCT and the dual-γ piecewise exponential transform

Shen, Jin; Ji, Chao; Yan, C.; Xing, Jinyu

doi:10.1016/j.precisioneng.2018.07.006

Cited by 22 publications

(17 citation statements)

References 8 publications

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“…Among these Mura, band-shape Mura and line-shape Mura are particularly difficult to be detected. A mainstream Mura detection method is to enhance Mura's feature directly, as described in researches [1,[4][5][6][7][8][9][10][11]. Different filters are applied on the images depending on cases, strengthening the boundary of Mura or making the characteristics of Mura more obvious.…”

Section: Introductionmentioning

confidence: 99%

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A Mura Detection Method Based on an Improved Generative Adversarial Network

Xie

Yang

Wang³

et al. 2021

IEEE Access

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Mura is defined as visual unevenness on the display panel. It can cause unpleasant feelings, so it is necessary to perform Mura inspection during the display quality test. However, Mura is quite difficult to be detected because of its irregular shape and size as well as its low contrast. To solve this practical problem, we proposed a GAN-based model named UADD-GAN to detect Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained using only normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and outputs their reconstructions, while the discriminator receives images and determines whether they are original or reconstructed ones, defiantly helping the generator to perform reconstructions better. The symmetric structure and the skip-adding operation make it easy for the UADD generator to reconstruct the normal samples well. In the detecting procedure, the generator performs worse in the reconstruction of samples with Mura so that we can distinguish them from the normal ones. To make full use of discriminator, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. Meanwhile, a two-side detecting method was used to detect Mura since all the samples are not in a square shape and it greatly improved the detecting accuracy. We have conducted experiments of Mura data sets with different proportion and our research indicates that our proposed method surpasses other state of the art methods.

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Section: Introductionmentioning

confidence: 99%

“…Another Mura detection method is based on background reconstruction [6,[11][12][13][14][15][16][17][18][19][20][21]. This kind of method firstly reconstructs the background of an image, and then obtains a residual image which contains Mura information by subtracting the background image from the original one.…”

Section: Introductionmentioning

confidence: 99%

A Mura Detection Method Based on an Improved Generative Adversarial Network

Xie

Yang

Wang³

et al. 2021

IEEE Access

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“…The manufacturing process of LCDs has many optical challenges, including the detection of defect regions, pixel failure (always off or on), mura (a non-uniformity distortion, in luminance or color), intensity, uniformity, and abnormal reproduction of colors (perceivable regional color variations) [6], [7]. The task of error detection can be carried out either visually or with specialized inspection equipment.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Neural Network for the Evaluation of Image Color Quality Shown on Liquid Crystal Displays

et al. 2021

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The color quality of an image shown on a liquid crystal display (LCD) can be measured with a spectroradiometer; however, this instrument is expensive, work under controlled illumination conditions with an artificial source of light, and measurements take a long time. A spectroradiometer returns measurements of wavelength or CIE color space. A low-cost and fast alternative consists of using a digital camera that outputs RGB measurements. Unfortunately, comparisons between measurements obtained with both instruments cannot be performed; hence, conversion equations must be used. The main problem is that equations do not consider the effects caused by the camera lens, sensor variations, and configurable parameters such as gain and the exposure time. This paper proposes the architecture of a radial basis function neural network (RBFNN) to measure the image color quality displayed by an LCD using a digital camera. The RGB values acquired with a camera are used as inputs to the RBFNN. The output predicted the luminance and chromaticity components in the CIExyY color space and included the corrections to the lens and camera parameters. First, the RBFNN topology is explained, including the calculation of the number of neurons in the hidden layer, and the definition of the dispersion centers and their associated spread. Next, the experiments related to RGB color space reconstruction and conversion from RGB to CIE are presented. The proposed approach was tested on a real automotive scenario. The results obtained were similar to those measured with the spectroradiometer with an accuracy of 93.3%. Moreover, the results remained within limits established by the six-sigma methodology.INDEX TERMS Liquid crystal display, radial basis function neural network, digital camera, spectroradiometer, RGB, CIE.

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“…Hence, some scholars adopted discrete cosine transfer (DCT) methods to reconstruct the illumination background of the defect image. The differences between the inspected image and the reconstructed background can be employed to obtain the defect region by gray segmentation [5], [6]. Luo et al [7] proposed a dynamic background compensation method to defect hotrolled flat steel images that suffer from uneven illumination.…”

Section: Introductionmentioning

confidence: 99%

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

Qiu

Tang

et al. 2020

IEEE Access

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Surface defect detection based on computer vision remains a challenging task due to the uneven illumination, low contrast and miscellaneous patterns of defects. Current methods usually present undesirable detection accuracy and lack adaptability for the various scenes. In the paper, the novel uneven illumination surface defects inspection (UISDI) method is proposed to address these issues. First, the multi-scale saliency detection (MSSD) method is proposed to construct a coarse defect map and obtain the corresponding background regions. Second, a novel background similarity prior-based intrinsic image decomposition model (BSIID) is applied to divide the defect image into a non-defective shading layer and a defective reflectance layer. An accelerated optimization solution is proposed to solve the minimization problem of the intrinsic image decomposition model. Last, the enhanced defect image is obtained by filtering the reflectance image and is then utilized to accurately segment the defect region from the coarse defect map. The experiments conducted using four real-world defect datasets demonstrate that the proposed method outperforms state-of-the-art methods.

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TFT-LCD mura defect detection using DCT and the dual-γ piecewise exponential transform

Cited by 22 publications

References 8 publications

A Mura Detection Method Based on an Improved Generative Adversarial Network

A Mura Detection Method Based on an Improved Generative Adversarial Network

Radial Basis Function Neural Network for the Evaluation of Image Color Quality Shown on Liquid Crystal Displays

Uneven Illumination Surface Defects Inspection Based on Saliency Detection and Intrinsic Image Decomposition

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