• Title/Summary/Keyword: Non-local menas

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TFT-LCD Defect Detection Using Mean Difference Between Local Regions Based on Multi-scale Image Reconstruction (로컬 영역 간 평균 화소값 차를 이용한 멀티스케일 기반의 TFT-LCD 결함 검출)

  • Jung, Chang-Do;Lee, Seung-Min;Yun, Byoung-Ju;Lee, Joon-Jae;Choi, Il;Park, Kil-Houm
    • Journal of Korea Multimedia Society
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    • v.15 no.4
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    • pp.439-448
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    • 2012
  • TFT-LCD panel images have non-uniform brightness, noise signal and defect signal. It is hard to divide defect signal because of non-uniform brightness and noise signal, so various divide methods have being developed. In this paper, we suggest method to divide defective regions on TFT-LCD panel image by estimating a menas of two different size of windows, which is suggested by Eikvil et al., and using difference of them. But in this method, the size of detectable defects is restricted by the size of window, hence it has inefficient problem that the size of window have to increase to divide a large defect region. To solve this problem we suggest an algorithm which can divide various size of defects, by using Multi-scale and restrict a detectable size of defects in each scale. To prove an efficiency of suggested algorithm, we show that resulting images of real TFT-LCD panel images and an artificial image with various defects.

Improved Nonlocal Means Algorithm for Image Denoising (영상 잡음 제거를 위해 개선된 비지역적 평균 알고리즘)

  • Park, Sang-Wook;Kang, Moon-Gi
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.1
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    • pp.46-53
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    • 2011
  • Nonlocal means denoising algorithm is one of the most widely used denoising algorithm. Because it performs well, and the theoretic idea is intuitive and simple. However the conventional nonlocal means algorithm has still some problems such as noise remaining in the denoised flat region and blurring artifacts in the denoised edge and pattern region. Thus many improved algorithms based on nonlocal means have been proposed. In this paper, we proposed new improved nonlocal means denoising algorithm by weight update through weights sorting and newly defined threshold. Updated weights can make weights more refined and definite, and denoising is possible without that artifacts. Experimental results including comparisons with conventional algorithms for various noise levels and test images show the proposed algorithm has a good performance in both visual and quantitative criteria.