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A detection statistic for random-valued impulse noise.

Yiqiu Dong1, Raymond H Chan, Shufang Xu

  • 1School of Mathematical Sciences, Peking University, Beijing 100871, China. dyiqiu@math.pku.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 5, 2007
PubMed
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This study introduces a novel image statistic to detect random-valued impulse noise, enabling effective identification of corrupted pixels. The developed two-stage denoising method significantly improves image restoration and noise detection accuracy.

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Random-valued impulse noise corrupts digital images, degrading visual quality and hindering subsequent analysis.
  • Existing denoising methods often struggle with high noise levels and preserving image details.

Purpose of the Study:

  • To propose a novel image statistic for accurate detection of random-valued impulse noise.
  • To develop a robust two-stage denoising method for images corrupted by this noise type.
  • To evaluate the proposed method's performance against existing techniques.

Main Methods:

  • A new image statistic is introduced to identify pixels affected by random-valued impulse noise.
  • The statistic is combined with an edge-preserving regularization technique in a two-stage denoising framework.

Related Experiment Videos

  • Extensive simulations are conducted to assess the method's efficacy.

    • Main Results:

    • The proposed image statistic effectively identifies a majority of noisy pixels.
    • The two-stage denoising method demonstrates powerful performance in restoring images with up to 60% random-valued impulse noise.
    • The method significantly outperforms several existing techniques in both image restoration and noise detection.

    Conclusions:

    • The developed image statistic and two-stage denoising approach offer a significant advancement in handling random-valued impulse noise.
    • The method provides superior image restoration and noise detection capabilities, particularly at high noise densities.
    • This work contributes a valuable tool for digital image processing applications requiring robust noise removal.