Context-based prediction filtering of impulse noise images

@article{Gellert2016ContextbasedPF,
  title={Context-based prediction filtering of impulse noise images},
  author={Arpad Gellert and Remus Brad},
  journal={IET Image Process.},
  year={2016},
  volume={10},
  pages={429-437}
}
A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented. The algorithm replaces the noisy pixel with the value occurring with the highest frequency, in the same context as the replaceable pixel. Since it is a context-based technique, it preserves the details in the filtered images better than other methods. In the aim of validation, the authors have compared the proposed method with several existing denoising methods, many of them… 

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References

SHOWING 1-10 OF 41 REFERENCES

Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter

A non-local algorithm for image denoising

  • A. BuadesB. CollJ. Morel
  • Computer Science
    2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
  • 2005
TLDR
A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.

Progressive switching median filter for the removal of impulse noise from highly corrupted images

A new median-based filter, progressive switching median (PSM) filter, is proposed to restore images corrupted by salt-pepper impulse noise. The algorithm is developed by the following two main

A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises

TLDR
A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise that removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level.

IMPULSE NOISE REMOVAL FROM HIGHLY CORRUPTED IMAGES USING NEW HYBRID TECHNIQUE BASED ON NEURAL NETWORKS AND SWITCHING FILTERS

TLDR
A technique based on impulse noise detection by means of a self-organizing neural network and a class of the switching filters that can remove impulse noise effectively while preserving details is proposed.

Applying an improved neural network to impulse noise removal

TLDR
A new noise removal algorithm based on improved neural network, is applied to remove the impulse noise of the digital images and results show that the new algorithm is more improved than the conventional filters.

A Detection Statistic for Random-Valued Impulse Noise

TLDR
By combining an image statistic for detecting random-valued impulse noise with an edge-preserving regularization, this paper obtains a powerful two-stage method for denoising random- valued impulse noise, even for noise levels as high as 60%.

Mixed impulse and Gaussian noise removal using detail-preserving regularization

TLDR
This work proposes a two-stage approach based on impulse detectors and detail-preserving regularization that is universally capable of removing various degrees of impulse noise and mixed noise, while preserving fine image details well.

Neural Networks Applied for impulse Noise Reduction from Digital Images

TLDR
The proposed method for detecting and removing impulse noise from digital images based on the combination of two Artificial Neural Networks is compared with other methods on literature in terms of visual judgment and also using a quantitative measure of PSNR - Peak Signal To Noise Ratio.