43(3):157–192.

Paper doi abstract bibtex

Paper doi abstract bibtex

Weighted Median (WM) filters have the robustness and edge preserving capability of the classical median filter and resemble linear FIR filters in certain properties. Furthermore, WM filters belong to the broad class of nonlinear filters called stack filters. This enables the use of the tools developed for the latter class in characterizing and analyzing the behavior and properties of WM filters, e.g. noise attenuation capability. The fact that WM filters are threshold functions allows the use of neural network training methods to obtain adaptive WM filters. In this tutorial paper we trace the development of the theory of WM filtering from its beginnings in the median filter to the recently developed theory of optimal weighted median filtering. Applications discussed include: idempotent weighted median filters for speech processing, adaptive weighted median and optimal weighted median filters for image and image sequence restoration, weighted medians as robust predictors in DPCM coding and Quincunx coding, and weighted median filters in scan rate conversion in normal TV and HDTV systems

@article{yinWeightedMedianFilters1996, title = {Weighted Median Filters: A Tutorial}, author = {Yin, Lin and Yang, Ruikang and Gabbouj, M. and Neuvo, Y.}, date = {1996-03}, journaltitle = {IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing}, volume = {43}, pages = {157--192}, issn = {1057-7130}, doi = {10.1109/82.486465}, url = {http://mfkp.org/INRMM/article/1401591}, abstract = {Weighted Median (WM) filters have the robustness and edge preserving capability of the classical median filter and resemble linear FIR filters in certain properties. Furthermore, WM filters belong to the broad class of nonlinear filters called stack filters. This enables the use of the tools developed for the latter class in characterizing and analyzing the behavior and properties of WM filters, e.g. noise attenuation capability. The fact that WM filters are threshold functions allows the use of neural network training methods to obtain adaptive WM filters. In this tutorial paper we trace the development of the theory of WM filtering from its beginnings in the median filter to the recently developed theory of optimal weighted median filtering. Applications discussed include: idempotent weighted median filters for speech processing, adaptive weighted median and optimal weighted median filters for image and image sequence restoration, weighted medians as robust predictors in DPCM coding and Quincunx coding, and weighted median filters in scan rate conversion in normal TV and HDTV systems}, keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-1401591,~to-add-doi-URL,integration-techniques,median,weighting}, number = {3} }

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