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Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a new group of switching vector filters based on non-causal linear prediction for the detection of impulse noise from colour images is presented, where the proposed filters utilize the linear prediction coefficients obtained from the block-by-block autocorrelation function to find prediction vector pixel value at the center of the filter window.
Abstract: A new group of switching vector filters based on the non-causal linear prediction for the detection of impulse noise from colour images is presented. The proposed filters utilise the non-causal linear prediction coefficients obtained from the block-by-block autocorrelation function to find prediction vector pixel value at the centre of the filter window. Thirteen prediction coefficients are selected from the autocorrelation matrix obtained from a block of an image, and these coefficients are used to predict all pixels in that block. The difference between the predicted pixel and the original decides whether the pixel is corrupted with impulse noise. Extensive simulation experiments indicate that the new vector filters outperform the other vector filters currently used to eliminate impulse noise from colour images.

19 citations

Journal ArticleDOI
TL;DR: The results of the experiment show that the proposed algorithm successfully combines the merits of the Wiener filter and sharpening and achieves a significant proficiency in the enhancement of degraded X-ray images exhibiting Poisson noise, blurriness, and edge details.
Abstract: To resolve the problems of Poisson/impulse noise, blurriness, and sharpness in degraded X-ray images, a novel and efficient enhancement algorithm based on X-ray image fusion using a discrete wavelet transform is proposed in this paper. The proposed algorithm consists of two basics. First, it applies the techniques of boundary division to detect Poisson and impulse noise corrupted pixels and then uses the Wiener filter approach to restore those corrupted pixels. Second, it applies the sharpening technique to the same degraded X-ray image. Thus, it has two source X-ray images, which individually preserve the enhancement effects. The details and approximations of these sources X-ray images are fused via different fusion rules in the wavelet domain. The results of the experiment show that the proposed algorithm successfully combines the merits of the Wiener filter and sharpening and achieves a significant proficiency in the enhancement of degraded X-ray images exhibiting Poisson noise, blurriness, and edge details.

19 citations

Proceedings ArticleDOI
03 Apr 2011
TL;DR: In this paper, a new approach for modeling temporal variations of noise and channel in indoor power line is proposed, where three-conductor power cable (phase, neutral and ground) is modeled by a RLCG circuit.
Abstract: Broadband Power Line Communication (PLC) technologies represent a main factor of the development of the digital convergence voice-data-video in the home environment. Such technologies use the power line network as a propagation and a communication medium whose quality depends mainly on the power grid topology, but also on the connected household electrical appliances which impedance and noise levels have a great impact on the PLC systems. In this paper, we propose a new approach for modeling temporal variations of noise and channel in indoor power line. Three-conductor power cable (phase, neutral and ground) is modeled by a RLCG circuit. RLCG parameters are deduced from the impedance measurement seen by the generator in open circuit and in short circuit. Cable model is validated in both time and frequency domains. Then, the temporal variation of periodic noise is investigated. The global model combining the channel model and the noise variations is validated by comparing simulated results with measurements.

19 citations

Journal ArticleDOI
TL;DR: Two models with variational framework of restoring color images with impulse noise are presented, Inspired by the expectation-maximization (EM) algorithm, and the superiority of the proposed models is that: the weighting functions can effectively detect the noise in the image; with the noise information, the proposed algorithm can automatically balance the regularity of the restored image and the fidelity term by updating the weighted functions and the control parameters.
Abstract: In this paper, a new variational framework of restoring color images with impulse noise is presented. The novelty of this work is the introduction of an adaptively weighting data-fidelity term in the cost functional. The fidelity term is derived from statistical methods and contains two weighting functions as well as some statistical control parameters of noise. This method is based on the fact that impulse noise can be approximated as an additive noise with probability density function (PDF) being the finite mixture model. A Bayesian framework is then formulated in which likelihood functions are given by the mixture model. Inspired by the expectation-maximization (EM) algorithm, we present two models with variational framework in this study. The superiority of the proposed models is that: the weighting functions can effectively detect the noise in the image; with the noise information, the proposed algorithm can automatically balance the regularity of the restored image and the fidelity term by updating the weighting functions and the control parameters. These two steps ensure that one can obtain a good restoration even though the degraded color image is contaminated by impulse noise with large ration (90% or more). In addition, the numerical implementation of this algorithm is very fast by using a split algorithm. Some numerical experimental results and comparisons with other methods are provided to show the significant effectiveness of our approach.

19 citations

Proceedings ArticleDOI
15 Apr 2013
TL;DR: Results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less Computational time and works well in removing salt and pepper noise at low, medium and high noise densities.
Abstract: Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise may occur during image acquisition, transmission or storage Noise should be removed in such a way that important information of image should be preserved. We can use so many algorithms for getting the original image, by removing salt and pepper noise from the corrupted images. In this paper an algorithm is proposed for the restoration of gray scale images that are highly corrupted by impulse noise (salt and pepper noise). There are two phases in the proposed algorithm. First phase detects whether the processing pixel is corrupted or not. In the Second phase it recreates the corrupted pixel by means of the proposed algorithm. This algorithm shows better results than the Standard Median Filter (MF), Center Weighed Median Filter(CWM), Adaptive Median Filter(AMF), Adaptive Center Weighed Median Filter (ACWM), Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetrical Trimmed Median Filter Algorithm(MDBUTMF). Obtained results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less Computational time and works well in removing salt and pepper noise at low, medium and high noise densities.

19 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202371
2022168
2021111
2020175
2019206
2018210