Towards a general theory of robust nonlinear filtering: Selection filters

Juan G. Gonzalez, Daniel L. Lau, Gonzalo R. Arce

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations


In this paper we introduce a general framework for edge preserving filters, derived from the powerful class of M-estimators. First, we show that under very general assumptions, any location estimator generates an edge preserving filter if we approximate the estimate by one of the input samples. Based on this premise, we propose the family of S-estimators or S-filters, as a selection-type class of filters arising from a computationally tractable `selectification' of location M-estimators. S-filters inherit the richness of the theory underlying the M-estimators framework, providing a very flexible family of robust estimators with edge preservation capabilities. Several properties of S-filters axe studied. Sufficient and necessary conditions are given for an S-filter to present edge enhancing capabilities, and several novel filters within this framework are introduced and illustrated. Data, figures and source code utilized in this paper are available at

Original languageEnglish
Pages (from-to)3837-3840
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1997
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: Apr 21 1997Apr 24 1997

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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