Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions

Aizenberg, Igor; Butakoff, Constantine; Paliy, Dmitriy

doi:10.1109/lsp.2004.838198

Cited by 58 publications

(22 citation statements)

References 11 publications

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“…Over the years, better noise removal methods with different kinds of noise detectors have been proposed, for example, switching median (SM) filter [5], multi-state median (MSM) filter [6], adaptive center weighted median (ACWM) filter [7], the peak-and-valley filter [8], [9], signal dependent rank order mean (SD-ROM) filter [10], conditional signal-adaptive median (CSAM) filter [11], the pixel-wise MAD (PWMAD) filter [12], modified threshold Boolean filter (TBF) [13], Jarque-Bera test based median (JM) filter [14], two-output nonlinear filter [15], and iterative median filter [16], etc. With the noise detector, these filters will check each pixel if it is corrupted or not.…”

mentioning

confidence: 99%

A Detection Statistic for Random-Valued Impulse Noise

Dong

Chan

2007

IEEE Trans. on Image Process.

248

176

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This paper proposes an image statistic for detecting random-valued impulse noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued impulse noise even for noise level as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection. Index Termsrandom-valued impulse noise, noise detector, edge-preserving regularization, image denoising.

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mentioning

confidence: 99%

A Detection Statistic for Random-Valued Impulse Noise

Dong

Chan

2007

IEEE Trans. on Image Process.

248

176

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“…Switching median (SM) filter [2], multistate median (MSM) filter [3], adaptive center weighted median (ACWM) filter [4], the peak-and-valley filter [5], [6], signal-dependent rank-order mean (SD-ROM) filter [7], conditional signaladaptive median (CSAM) filter [8], the pixel-wise MAD (PWMAD) filter [9], modified threshold Boolean filter (TBF) [10], Jarque-Bera test based median (JM) filter [11], twooutput nonlinear filter [12], iterative median filter [13], etc. With the noise detector, these filters will check each pixel if it is corrupted or not.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Salt and Pepper Noise Removal using ROR and Contourlet Transform

MohanaDhas¹,

Suganthi²

2013

IJCA

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ROR and Contourlet transform is proposed to restore images corrupted by salt and pepper impulse noise. The operation is carried out in two stages, i.e., detection followed by filtering. For detection first, we propose the robust outlyingness ratio (ROR) for measuring how impulse like each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, proposed algorithm using contourlet transform (CT). Simulation results demonstrate that the proposed algorithm is better than traditional filters and is particularly effective for the cases where the images are very highly corrupted.

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“…[4,5]). To remove random impulse noises, the trilateral filter recently proposed by Garnett et al [1], and its variants [6], have proven very powerful; see also [7][8][9][10][11][12][13][14] for some other methods. The trilateral filter has also been the most efficient method to remove a mixed noise composed of a Gaussian noise and a random impulse noise.…”

Section: Introductionmentioning

confidence: 99%

A new method for removing mixed noises

Liu

et al. 2010

Sci. China Inf. Sci.

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We first introduce a similarity assumption to describe the similarity phenomenon in natural images, and establish a similarity principle which supplies a simple mathematical justification for the non-local means filter in removing Gaussian noises. Using the similarity principe in an adapted way, we then propose a new algorithm, called mixed noise filter (MNF) to remove simultaneously a mixture of Gaussian and random impulse noises. Our experiments show that our new filter improves significantly the trilateral filter in removing mixed noises, and that it is as efficient as the non-local means filter in removing Gaussian noises, and as good as the trilateral filter in removing random impulse noises.

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Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions

Cited by 58 publications

References 11 publications

A Detection Statistic for Random-Valued Impulse Noise

A Detection Statistic for Random-Valued Impulse Noise

A Novel Approach for Salt and Pepper Noise Removal using ROR and Contourlet Transform

A new method for removing mixed noises

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