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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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Proceedings ArticleDOI
22 May 2011
TL;DR: Improved signal to noise ratios and perceived audio quality is shown by explicitly modelling impulses with a discrete switching process and a new heavy-tailed amplitude model.
Abstract: We present a method for the removal of noise including non-Gaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regression model [1]. The problem is formulated in a joint Bayesian framework and we use a Gibbs MCMC sampler to estimate parameters. We show how to deal with variable magnitude impulses using a shifted inverse gamma distribution for their variance. Our results show improved signal to noise ratios and perceived audio quality by explicitly modelling impulses with a discrete switching process and a new heavy-tailed amplitude model.

21 citations

Journal ArticleDOI
TL;DR: An interconnected type-1 fuzzy algorithm trained by a modified version of the Scaled Conjugated Gradient method for impulsive noise cancellation in discrete multitone/orthogonal frequency-division multiplexing (DMT/OFDM)-based systems for broadband power line communications outperforms the standard impulsive noises techniques and other computational intelligence-based techniques.
Abstract: This paper introduces an interconnected type-1 fuzzy algorithm which is trained by a modified version of the Scaled Conjugated Gradient method for impulsive noise cancellation in discrete multitone/orthogonal frequency-division multiplexing (DMT/OFDM)-based systems for broadband power line communications. The advanced algorithm makes use of the fuzzy systems capacity of dealing with uncertainties to reduce the presence of high-power impulsive noises while the DMT/OFDM technique copes with the severe intersymbol interference observed in power line channels. As a result, for a given error probability, a high number of bits can be allotted to each subchannel due to the signal-to-noise ratio enhancements achieved by the proposed fuzzy algorithm. The simulation results show that the novel fuzzy algorithm not only achieve a high data rate, but it also outperforms the standard impulsive noises techniques and other computational intelligence-based techniques, especially in the presence of additive and high-power impulsive noises.

21 citations

Proceedings ArticleDOI
13 May 2008
TL;DR: A novel method based on efficient noise detection algorithm for effectively denoising extremely corrupted images and better details and edges preservation is proposed in this paper.
Abstract: To restore images corrupted by impulse noise, various image filtering strategies have been proposed. However, a common drawback of these methods is that the details and edges can not recovered satisfactorily when the noise level is high. A novel method based on efficient noise detection algorithm is proposed in this paper for effectively denoising extremely corrupted images and better details and edges preservation. The proposed method replaces only noisy pixel by either the median value or by a noise-free pixel value inside the selected filtering window. While uncorrupted pixels are remain unchanged. The key feature of the proposed method is very high noise detection accuracy, yielding zero miss-detection rate. Comparative studies of the proposed method and other methods for different images are made and the advantages of the proposed method are fully demonstrated.

21 citations

Journal ArticleDOI
TL;DR: The authors' model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels.
Abstract: Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors' model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high-density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high-density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.

21 citations

Journal ArticleDOI
TL;DR: The experimental outcomes demonstrate that the proposed approach is better than those existing methods in terms of standard signal-to-noise ratio (PSNR), relative error (ReErr), and visual quality.

21 citations


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