An iterative procedure for removing random-valued impulse noise

Raymond H. CHAN; Chen HU; Mila NIKOLOVA

doi:10.1109/LSP.2004.838190

An iterative procedure for removing random-valued impulse noise

Raymond H. CHAN^*
, Chen HU
, Mila NIKOLOVA

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

195 Citations (Scopus)

Abstract

This paper proposes a two-stage iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. These two phases are applied alternatively. Simulation results indicate that the proposed method is significantly better than those using just nonlinear filters or regularization only.

Original language	English
Pages (from-to)	921-924
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	11
Issue number	12
DOIs	https://doi.org/10.1109/LSP.2004.838190
Publication status	Published - Dec 2004
Externally published	Yes

Funding

Manuscript received December 12, 2003; revised April 28, 2004. This work was supported by HKRGC Grant CUHK4243/01P and CUHK DAG 2060220. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Steven L. Grant.

Keywords

Adaptive center-weighted median filter
Impulse noise
Regularization methods

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10.1109/LSP.2004.838190

Cite this

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AB - This paper proposes a two-stage iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. These two phases are applied alternatively. Simulation results indicate that the proposed method is significantly better than those using just nonlinear filters or regularization only.

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KW - Regularization methods

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An iterative procedure for removing random-valued impulse noise

Abstract

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Keywords

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