Fast rank algorithms based on multiscale histograms and lazy calculations

Authors:
M. V. Storozhilova;D. V. Yurin
Affiliations:
Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetic, Lomonosov Moscow State University, Leninskie Gory, Moscow, Russia 119991;Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetic, Lomonosov Moscow State University, Leninskie Gory, Moscow, Russia 119991
Venue:
Pattern Recognition and Image Analysis
Year:
2013

Citing 4
Cited 0

Rank algorithms for picture processing

Computer Vision, Graphics, and Image Processing
Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast median and bilateral filtering

ACM SIGGRAPH 2006 Papers
Median Filtering in Constant Time

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.