Fuzzy random impulse noise reduction method

Schulte, Stefan; Witte, Valérie De; Nachtegael, Mike; Weken, Dietrich Van der; Kerre, Etienne

doi:10.1016/j.fss.2006.10.010

Cited by 103 publications

(70 citation statements)

References 18 publications

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“…-FUZZY: the GOA filter [26], FRINRM [27] (fuzzy randomly valued impulse noise reduction method), HAF [28] (histogram adaptive fuzzy), EIFCF [29] (extended iterative fuzzy control based filter), SFCF [29] (smoothing fuzzy control based filter), DWMAV [30] (decreasing weight fuzzy filter with moving average centre), GMAV [30] (Gaussian fuzzy filter with moving average centre), AFSF [31] (the adaptive fuzzy switching filter), FSB [32,33] (fuzzy similarity filter) and AWFM [34,35] (adaptive weighted fuzzy mean).…”

Section: Resultsmentioning

confidence: 99%

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

Schulte

Huysmans

Pižurica

et al. 2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.

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Section: Resultsmentioning

confidence: 99%

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

Schulte

Huysmans

Pižurica

et al. 2006

Advanced Concepts for Intelligent Vision Systems

Self Cite

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“…. , N }, and ν i,j is an identically distributed, independent random process with and arbitrary underlying probability density function [20], that is the intensity value of the noisy pixel. There are two types of impulse noise, one is the "salt and pepper" noise and the other is random-valued impulse noise.…”

Section: Impulse Noise Modelsmentioning

confidence: 99%

High-density impulse noise removal using fuzzy mathematical morphology

González-Hidalgo¹,

Massanet²,

Mir³

et al. 2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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This paper proposes a filtering method for highdensity impulse noise removal based on the fuzzy mathematical morphology using t-norms. The method is a two phased method. In the first phase, an impulse noise detector based on the fuzzy tophat transforms is used to identify pixels which are likely to be contaminated by noise. In the second phase, the image is restored using a specialized regularization method using fuzzy open-close or fuzzy close-open sequences applied only to those selected contaminated pixels and applying then a block smart erase algorithm. Experimental results show that the proposed algorithm presents a better performance in terms of edge preservation and noise suppression than other nonlinear filtering methods, including the presented in [1], in which this method is based on.

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“…Various designs based on the concepts derived from the fuzzy sets theory combined with the order statistics have been also described [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Another family of techniques aimed at the improvement of the detail preservation of the filters based on reduced ordering is utilizing the concept of vector weighting, which privileges the central pixel of the processing window [50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Adaptive rank weighted switching filter for impulsive noise removal in color images

Smołka

Malik

2012

J Real-Time Image Proc

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In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process. The introduced adaptive design enables the filter to tune its parameters to the amount of impulsive noise corrupting the image. The comparison with existing denoising schemes shows that the new technique more efficiently removes the impulses introduced by the noise process, while better preserving image details. An important feature of the new filter is its low computational complexity, which allows for its application in real-time applications.

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Fuzzy random impulse noise reduction method

Cited by 103 publications

References 18 publications

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

High-density impulse noise removal using fuzzy mathematical morphology

Adaptive rank weighted switching filter for impulsive noise removal in color images

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