Automatic Mura Detection for Display Film Using Mask Filtering in Wavelet Transform

Park, Jong-Seung; Lee, Seungho

doi:10.1587/transinf.2014edl8140

Cited by 8 publications

(4 citation statements)

References 7 publications

(9 reference statements)

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“…To accommodate the differences in defect morphology, they optimized the algorithm by combining and parallel implementing both methods. Park et al [20] improved the background reconstruction method based on discrete cosine transform to achieve more accurate defect detection results. They applied discrete wavelet transform to the filtered background image and used a region-growing method to generate the defect image.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…The detection results of the algorithm in this paper are shown below, where the DCTbased background reconstruction method is compared separately [14] to the polynomial fitting-based background reconstruction method [20].…”

Section: Comparison Of Algorithm Detection Effectmentioning

confidence: 99%

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

2023

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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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A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision

Zhang

Huang

et al. 2020

Computers & Industrial Engineering

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Automatic Mura Detection for Display Film Using Mask Filtering in Wavelet Transform

Cited by 8 publications

References 7 publications

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

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

A Mura Detection Method Based on an Improved Generative Adversarial Network

A novel algorithm for defect extraction and classification of mobile phone screen based on machine vision

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