Closest-to-mean filter: An edge preserving smoother for Gaussian environments

Daniel Leo Lau, Juan Guillermo Gonzalez

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Median based filters have gained wide-spread use because of their ability to preserve edges and suppress impulses. In this paper, we introduce the Closest-to-Mean (CTM) filter, which outputs the input sample closest to the sample mean. The CTM filtering framework offers lower computational complexity and better performance in near Gaussian environments than median filters. The formulation of the CTM is derived from the theory of S-filters, which form a class of generalized selection-type filters with the features of edge preservation and impulse suppression. S-filters can play a significant role in image processing, where edge and detail preservation are of paramount importance. We compare the performance of CTM, median, and mean filters in the smoothing of edges and impulses immersed in Gaussian noise. A sufficient condition for a signal to be a root of the CTM filter is included. Data, figures and source code utilized in this paper are available at

Original languageEnglish
Pages (from-to)2593-2596
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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