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Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

IEEE Transactions on Image Processing · 2005 · Vol. 14(10) · pp. 1479–1485
Raymond H. ChanChung-WaMila Nikolova

Abstract

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.

Image and Signal Denoising MethodsAdvanced Image Processing TechniquesSparse and Compressive Sensing TechniquesSalt-and-pepper noiseMedian filterImpulse noiseValue noiseGradient noiseGaussian noiseNoise (video)Regularization (linguistics)MathematicsImage noise

MeSH terms

AlgorithmsArtificial IntelligenceComputer SimulationImage EnhancementImage Interpretation, Computer-AssistedPattern Recognition, AutomatedStochastic ProcessesModels, StatisticalInformation Storage and RetrievalArtifacts
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1,134
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References
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing · 1997 · 1,261 citations
Adaptive median filters: new algorithms and results
IEEE Transactions on Image Processing · 1995 · 1,185 citations
Bayesian reconstructions from emission tomography data using a modified EM algorithm
IEEE Transactions on Medical Imaging · 1990 · 1,231 citations
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Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization · Scinovex