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Denoising of digital images through PSO based pixel classification

Somnath Mukhopadhyay
  • Dept. of Computer Science & Engineering, Aryabhatta Institute of Engineering & Management, West Bengal, 713148, Durgapur, India
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  • De Gruyter OnlineGoogle Scholar
/ Jyotsna Mandal
Published Online: 2013-12-28 | DOI: https://doi.org/10.2478/s13537-013-0111-3


This paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

Keywords: image de-noising; mean square error; particle swarm optimization; peak signal to noise ratio; random valued noise; salt and pepper noise; training image

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About the article

Published Online: 2013-12-28

Published in Print: 2013-12-01

Citation Information: Open Computer Science, Volume 3, Issue 4, Pages 158–172, ISSN (Online) 2299-1093, DOI: https://doi.org/10.2478/s13537-013-0111-3.

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