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Fast rank algorithms based on multiscale histograms and lazy calculations

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Abstract

Rank algorithms allow effective solutions for image smoothing and impulse noise suppression, but most of them are computationally complex. On the base of multiscale histograms, we propose algorithms for fast computations of EV and KNV neighborhood average, sliding equalization and search for an arbitrary element in a rank series (median filtering is a particular case of this algorithm). An approach using lazy calculations for fast updating of multiscale histograms is proposed. Using the developed algorithms, we have achieved a processing speed for EV and KNV neighborhood average algorithms that is close to the fastest known median filtering algorithm.

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Author information

Correspondence to M. V. Storozhilova.

Additional information

The article was translated by the authors.

Maria V. Storozhilova. Born 1991. Student at the chair of Mathematical Physics, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia.

Areas of interest: mathematical methods of image processing, computer vision, image filtering and enhancement, noise suppression, medical image processing, modern programming techniques.

Dmitry V. Yurin. Born 1965. PhD, senior researcher at the laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia.

Areas of interest: mathematical methods of image processing, computer vision, primary information feature detection, image filtering, 3D recovery, image segmentation, image registration and mosaicing, modern programming techniques.

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Storozhilova, M.V., Yurin, D.V. Fast rank algorithms based on multiscale histograms and lazy calculations. Pattern Recognit. Image Anal. 23, 367–374 (2013). https://doi.org/10.1134/S1054661813030127

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Keywords

  • rank algorithm
  • image denoising
  • image sharpening
  • image filtering
  • lazy calculations