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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: This paper presents an implementation of the PARIGI method, which relies on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch.
Abstract: In this paper, we present an implementation of the PARIGI method that addresses the problem of the restoration of images affected by impulse noise or by a mixture of Gaussian and impulse noise. The method relies on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are performed in the case of pure impulse noise and in the case of a mixture of Gaussian and impulse noise.

15 citations

Journal ArticleDOI
TL;DR: Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details and operate more efficiently.
Abstract: We proposed an automatic method to improve performance of iterative noise filters.The iterative noise filters with the automatic method can process in finite steps.This method showed better implementation for de-noising in experimental results. Whether input images are corrupted by impulse noise and what the noise density level is are unknown a priori, and thus published iterative impulse noise filters cannot adaptively reduce noise, resulting in a smoothing image or unclear de-noising. For this reason, this paper proposes an automatic filtering convergence method using PSNR checking and filtered pixel detection for iterative impulse noise filters. (1) First, the similarity between the input image and the 1st filtered image is determined by calculating MSE. If MSE is equal to 0, then the input image is unfiltered and becomes the output. (2) Otherwise, one applies PSNR checking and filtered pixel detection to estimate the difference between the tth filtered image and the t-1th filtered image. (3) Finally, an adaptive and reasonable threshold is defined to make the iterative impulse noise filters stop automatically for most image details preservation in finite steps. Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details. In addition, iterative impulse noise filters operate more efficiently.

15 citations

Proceedings ArticleDOI
01 Nov 2018
TL;DR: Experimental results show that the method based on adaptive median filter for fingerprint image enhancement outperforms the traditional median filtering method in filtering impulse noise performance.
Abstract: The traditional median filtering method uses a fixed filter window size method to remove the impulse noise in a fingerprint image. If the filtering window size is small, the traditional median filtering method will not filter out the impulse noise completely. If the filtering window size is large, the fingerprint image may become blurred. To solve the problem, a method based on adaptive median filter is proposed for fingerprint image enhancement processing and impulse noise removal in the paper. The use of adaptive median filtering to remove the impulse noise of the fingerprint image mainly involves three steps. First, the size of the adaptive median filter window is initialized, and it is judged whether the center pixel of the filter window in the fingerprint image is impulse noise. Second, the size of the filter window is determined based on the median value, the maximum value, and the minimum value within the filter window. Finally, median filtering is performed on the fingerprint image under the filter window size obtained in the previous steps, and the filter output value is used instead of the window center pixel value. The method is tested on rolled fingerprint images contaminated by impulse noise and fingerprint images contaminated by impulse noise from a crime scene. Experimental results show that the method based on adaptive median filter for fingerprint image enhancement outperforms the traditional median filtering method in filtering impulse noise performance.

15 citations

Proceedings ArticleDOI
01 Feb 2007
TL;DR: This paper presents a system with auto selection technique to reduce impulsive noise effect in wireless communication systems and results for QAM and FSK through simulation as well as real-time implementation on an FPGA development system are presented and show marked improvement in the bit error rate.
Abstract: This paper presents a system with auto selection technique to reduce impulsive noise effect in wireless communication systems. The impulsive noise is detected through comparison with a fixed reference. The selection of the appropriate reduction technique to be applied is based on an estimation of the rate of impulsive noise occurrence. The proposed system uses subtraction-gating for low occurrence rate, conventional limiting for high occurrence rate, and passes the incoming signal to the output in the absence of impulsive noise. The results for QAM and FSK through simulation as well as real-time implementation on an FPGA development system are presented and show marked improvement in the bit error rate in each case.

15 citations

Journal ArticleDOI
TL;DR: ME-HM outperforms all existing eigen Value-based sensing schemes including the maximum-eigenvalue-geometric-mean (ME-GM) algorithm previously presented by the authors of this paper, for smaller sample length, low signal-to-noise ratio (SNR) and increased number of cooperative secondary users.
Abstract: The detection of spectrum ‘holes’ is one of the primary tasks of a cognitive radio (CR). Blind detection techniques using eigenvalues have attracted a great amount of interest because of the fact that no a-priori knowledge is needed. The majority of the literature only considers the effect of thermal noise in detector performance; however in practice, man-made noise also exists and degrades detector performance. This motivated one to consider the performance of blind eigenvalue-based spectrum sensing for the dual condition of thermal (Gaussian) and man-made (impulse) noise. In the literature, most of the sensing schemes perform very well for large sample length. This can be achieved either by increasing the sensing duration or by oversampling the signal. The former increases the frequency of missed opportunities, whereas the latter causes samples to become highly correlated thus degrading the detector performance. Hence, the authors are motivated to investigate algorithms that perform well for smaller sample length. Three new eigenvalue-based sensing algorithms are proposed, viz. maximum-eigenvalue-harmonic-mean (ME-HM), maximum-eigenvalue-contra-harmonic-mean-p (ME-CHM-p) and contra-harmonic-mean-minimum-eigenvalue (CH-ME). ME-HM outperforms all existing eigenvalue-based sensing schemes including the maximum-eigenvalue-geometric-mean (ME-GM) algorithm previously presented by the authors of this paper, for smaller sample length, low signal-to-noise ratio (SNR) and increased number of cooperative secondary users. The ME-CHM-p algorithm, which only allows negative values of p, the filter order, performs identically to ME-HM when p=−1. However, for smaller values of p the probability of detection (PD) improves significantly more until p=−5. The CH-ME algorithm also exhibits an improvement in PD but does not outperform ME-GM. In addition, the proposed schemes and ME-GM exhibit a significant degree of immunity to impulse noise compared to existing schemes. The analytical and simulation results are presented for the proposed schemes and ME-GM for the Gaussian and impulse noise scenario in the Nakagami-m fading channel. In addition, the algorithms are evaluated for wireless microphone (WM) signals and show improved performance. The ME-CHM-p algorithm performed the best compared with other algorithms, for small and large sample lengths, low SNR, correlated signals and for increased number of cooperating secondary radios, with and without impulse noise.

15 citations


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