P‐85: A New Method for Color Mura Quantification

Lee, Mintak; Kim, Soo‐Young; Kim, Kyuseok; Shin, Woong‐Chul; Choe, Wonyoung; Kwag, Jinoh

doi:10.1002/sdtp.11952

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…In the display industry, this word has been adopted as the name for irregularities and "clouding" effects seen on OLED screens [21]. Efforts such as adjusting driving circuit have been made to evaluate and decrease mura effect on OLED screens [22][23][24]. Meanwhile, the elimination of non-unform function layers maybe more effective.…”

Section: Discussionmentioning

confidence: 99%

P‐14.1: Realization of the Unexpected Pattern in Solution‐Processed OLED

Zhong

Peng

et al. 2022

Symp Digest of Tech Papers

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Section: Discussionmentioning

confidence: 99%

P‐14.1: Realization of the Unexpected Pattern in Solution‐Processed OLED

Zhong

Peng

et al. 2022

Symp Digest of Tech Papers

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“…However, their method did not normalize the CSF filtering function, rendering the final evaluation metrics unusable. Lee et al [23] proposed a background reconstruction method in the XYZ to Lab color space for Mura defect detection. However, their method utilized absolute just noticeable difference (JND) for defect segmentation, resulting in relatively low defect detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

Color Mura Defect Detection Method Based on Channel Contrast Sensitivity Function Filtering

2023

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To address the issue of low detection accuracy caused by low contrast in color Mura defects, this paper proposes a color Mura defect detection method based on channel contrast sensitivity function (CSF) filtering. The RGB image of the captured liquid crystal display (LCD) display is converted to the Lab color space, and the Weber contrast feature maps of the Lab channel images are calculated. Frequency domain filtering is performed using the CSF to obtain visually sensitive Lab feature maps. Color Mura defect detection is achieved by employing adaptive segmentation thresholds based on the fused feature maps of the L channel and ab channels. The color Mura evaluation criterion is utilized to quantitatively assess the defect detection results. Experimental results demonstrate that the proposed method achieves an accuracy rate of 87% in color Mura defect detection, outperforming existing mainstream detection methods.

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“…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%

“…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. For example, researches [11,14,15,18,19] reconstruct the image background using discrete cosine transform(DCT). [13] generates the background image with average filter.…”

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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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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P‐85: A New Method for Color Mura Quantification

Cited by 3 publications

References 6 publications

P‐14.1: Realization of the Unexpected Pattern in Solution‐Processed OLED

P‐14.1: Realization of the Unexpected Pattern in Solution‐Processed OLED

Color Mura Defect Detection Method Based on Channel Contrast Sensitivity Function Filtering

A Mura Detection Method Based on an Improved Generative Adversarial Network

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