Recursive order-statistic soft morphological filters

Soo Chang Pel, Chin Lun Lai, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review


In this paper, a new class of the recursive order-statistic soft morphological (ROSSM) filters are proposed, and their important properties related to the morphological filtering are developed. Criteria for optimal selection of parameters are provided in order to achieve excellent performance in noise reduction and edge preservation. It is shown through experimental results that the ROSSM filters, compared with the order-statistic soft morphological filters or other well-known nonlinear filters, have a better outcome in signal reconstruction. Two examples are used for the extension of the proposed filters to illustrate their flexibilty.

Original languageEnglish (US)
Pages (from-to)819
Number of pages1
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Recursive order-statistic soft morphological filters'. Together they form a unique fingerprint.

Cite this