Adaptive Filtering Using Channel Representations

Abstract

This review article gives an overview on adaptive filtering methods based on channel representations. The framework of channel representations and its relation to density estimation is introduced. The fast and accurate scheme of virtual shift decoding is introduced and applied in several variants of channel smoothing:
  • channel smoothing with alpha-synthesis for improving stability of edge-enhancing filtering

  • orientation adaptive channel smoothing with applications to coherence-enhancing filtering

  • channel smoothing using graph-cuts for improving filtering results at corners

  • channel-coded feature maps (CCFMs) which lead to a significant speed-up of channel averaging

  • CCFM-based smoothing based on optimal parameters derived from a novel uncertainty relation

For each method, an algorithmic description and some examples of results are provided, together with discussions and references of the original papers. Cross connections to other articles in this volume are given where appropriate.

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Computer Vision Laboratory, Department of Electrical EngineeringLinköping UniversityLinköpingSweden

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