Iterative mixed norm image restoration algorithm

Min Cheol Hong*, Tania Stathaki, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

In this paper, we propose an iterative mixed norm image restoration algorithm. A functional which combines the least mean squares (LMS) and the least mean fourth (LMF) functionals is proposed. A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed. Experimental results demonstrate the capability of the proposed approach.

Original languageEnglish (US)
Pages137-141
Number of pages5
StatePublished - Jan 1 1997
EventProceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS - Banff, Can
Duration: Jul 21 1997Jul 23 1997

Other

OtherProceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS
CityBanff, Can
Period7/21/977/23/97

ASJC Scopus subject areas

  • Signal Processing

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    Hong, M. C., Stathaki, T., & Katsaggelos, A. K. (1997). Iterative mixed norm image restoration algorithm. 137-141. Paper presented at Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS, Banff, Can, .