Fast Rank Algorithms Based on Multiscale Histograms and Lazy Calculations
Publication type
Journal article
Author(s)
M. V. Storozhilova, D. V. Yurin
Publication date
2013
Journal
Pattern Recognition and Image Analysis
Volume
23
Issue
3
Page(s)
367–374
Publisher
Pleiades Publishing, Ltd.
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.
