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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.


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Journal ArticleDOI
TL;DR: The detector efficiently extracts edges in digital images corrupted by impulse noise without requiring the filtering of the noise.
Abstract: A new neuro-fuzzy edge detector for digital images corrupted by impulse noise is presented. The structure of the detector is very simple and comprises four identical neuro-fuzzy subdetectors and a postprocessor. The internal parameters of the detector are determined by training. The detector efficiently extracts edges in digital images corrupted by impulse noise without requiring the filtering of the noise.

31 citations

Proceedings ArticleDOI
04 Jun 2007
TL;DR: In this paper, an impulsive noise measurement system has been designed and built, which meets and improves the main features of classical equipments used to measure noise, offering both in-phase and quadrature outputs simultaneously.
Abstract: Measurements of radio impulsive noise in industrial and urban electromagnetic environments have been conducted and its effect on radio communication systems is studied. An impulsive noise measurement system has been designed and built. It meets and improves the main features of classical equipments used to measure noise, offering both in-phase and quadrature outputs simultaneously. The system was carefully calibrated before a measurement campaign was conducted in industrial and urban environments to get impulsive noise statistics. Results show that shot noise events are less frequent in the industrial than at the urban environment, but when a noise event occurs, noise pulses appear more grouped in bursts, exhibit larger amplitude and wider bandwidth. In both cases, shot noise pulses have very high amplitude, long duration and high repetition rate, so the performance of radio communication systems could be significantly reduced in the industrial environment. The effect of the noise burst on digital radio communication systems using different types of modulation is analyzed.

31 citations

Journal ArticleDOI
TL;DR: A multiuser receiver that involves an adaptive nonlinear preprocessing front-end based on a multilayer perceptron neural network, which acts as a mechanism to reduce the influence of impulsive noise followed by a postprocessing stage using linear adaptive filters for MAI suppression.
Abstract: Multiuser communications channels based on code division multiple access (CDMA) technique exhibit non-Gaussian statistics due to the presence of highly structured multiple access interference (MAI) and impulsive ambient noise. Linear adaptive interference suppression techniques are attractive for mitigating MAI under Gaussian noise. However, the Gaussian noise hypothesis has been found inadequate in many wireless channels characterized by impulsive disturbance. Linear finite impulse response (FIR) filters adapted with linear algorithms are limited by their structural formulation as a simple linear combiner with a hyperplanar decision boundary, which are extremely vulnerable to impulsive interference. This raises the issues of devising robust reception algorithms accounting at the design stage the non-Gaussian behavior of the interference. We propose a multiuser receiver that involves an adaptive nonlinear preprocessing front-end based on a multilayer perceptron neural network, which acts as a mechanism to reduce the influence of impulsive noise followed by a postprocessing stage using linear adaptive filters for MAI suppression. Theoretical arguments supported by promising simulation results suggest that the proposed receiver, which combines the relative merits of both nonlinear and linear signal processing, presents an effective approach for joint suppression of MAI and non-Gaussian ambient noise.

31 citations

Journal ArticleDOI
Hongyan Zhang1, Cai Jingyi1, Wei He, Huanfeng Shen1, Liangpei Zhang1 
TL;DR: The HSI observation model is extended and a double low-rank (DLR) matrix decomposition method is proposed for HSI denoising and destriping to achieve separation of the noise-free HSI, stripe noise, and other mixed noise.
Abstract: Hyperspectral images (HSIs) have a wealth of applications in many areas, due to their fine spectral discrimination ability. However, in the practical imaging process, HSIs are often degraded by a mixture of various types of noise, for example, Gaussian noise, impulse noise, dead pixels, dead lines, and stripe noise. Low-rank matrix decomposition theory has been widely used in HSI denoising, and has achieved competitive results by modeling the impulse noise, dead pixels, dead lines, and stripe noise as sparse components. However, the existing low-rank-based methods for HSI denoising cannot completely remove stripe noise when the stripe noise is no longer sparse. In this article, we extend the HSI observation model and propose a double low-rank (DLR) matrix decomposition method for HSI denoising and destriping. By simultaneously exploring the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HSI, the two low-rank constraints are formulated into one unified framework, to achieve separation of the noise-free HSI, stripe noise, and other mixed noise. The proposed DLR model is then solved by the augmented Lagrange multiplier (ALM) algorithm efficiently. Both simulation and real HSI data experiments were carried out to verify the superiority of the proposed DLR method.

31 citations

Journal ArticleDOI
TL;DR: A novel Adaptive Switching Modified Decision Based Unsymmetric Trimmed Median Filter for noise reduction in gray scale MR Images which are affected by salt and pepper noise is proposed.

31 citations


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