Efficient Impulsive Noise Suppression Via Nonlinear Recursive Filtering

Yong Hoon Lee, Sung Jea Ko, Adly T. Fam

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


A nonlinear recursive filter for the suppression of impulsive noises is proposed. This filter selects from each window a sample closest in value to the most recent output, and is thus named the lust output reference (LOR) filter. A relationship between the LOR and recursive median filters is derived, and some statistical properties are studied through computer simulations. The results indicate that this filter preserves edges while suppressing impulsive noise. It is shown that LOR filters are more effective in suppressing impulses, and are often simpler to implement than median filters.

Original languageEnglish
Pages (from-to)303-306
Number of pages4
JournalIEEE Transactions on Acoustics, Speech, and Signal Processing
Issue number2
Publication statusPublished - 1989 Feb
Externally publishedYes

Bibliographical note

Funding Information:
I. INTRODUCTION In digital signal processing, signals are sometimes corrupted by impulsive noise that appears as very large spikes of short duration. For example, in digital image or speech communications, channel transmission errors usually result in noise impulses in the received signal [ 11-[3]. Various restoration techniques have been proposed for the suppression of impulses. Early techniques apply a linear operator such as averaging prior to using a threshold algorithm [2]. Since linear operators are sensitive to impulses, the performance of such restoration methods deteriorates rapidly as the probability of impulse occurrence increases. In addition, their performance is Manuscript received August 11, 1986; revised June 16, 1988, This work was by the National Science Foundation under Grant DCI- 8-6-1. 1. -8 .5-9 . The authors are with the Department of Electrical and Computer Engineering, State University of New York at Buffalo, Buffalo, NY 14260. IEEE Log Number 8825142.

ASJC Scopus subject areas

  • Signal Processing


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