Impulse noise

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

Improved Image Recovery From Compressed Data Contaminated With Impulsive Noise

Abstract: Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the l2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.

Cyclic spectral analysis of power line noise in the 3–200 kHz band

Abstract: Narrowband OFDM Power Line Communication (NB-OFDM PLC) systems are a key component of current and future smart grids. NB-OFDM PLC systems enable next-generation smart metering, distributed control, and monitoring applications over existing power delivery infrastructure. It has been shown that the performance of these systems is severely limited by impulsive, non-Gaussian additive noise. A substantial component of this noise has time-periodic statistics (i.e. it is cyclostationary) synchronous to the AC mains cycle. In this work, we analyze the cyclic structure of power line noise observed in a G3 PLC system operating in the CENELEC 3-148.5 kHz band. Our contributions include: (i) the characterization of noise measurements in several urban usage environments, (ii) the development of a cyclic bit loading method for G3, and (iii) the quantification of its throughput gains over measured noise. Through this analysis, we confirm strong cyclostationarity in power lines and identify several sources of the cyclic noise.

Expected absolute value estimators for a spatially adapted regularization parameter choice rule in L 1 -TV-based image restoration

Abstract: A total variation (TV) model with an L 1 -fidelity term and a spatially adapted regularization parameter is presented in order to reconstruct images contaminated by impulse noise. This model intends to preserve small details while homogeneous features still remain smooth. The regularization parameter is locally adapted according to a local expected absolute value estimator depending on the statistical characteristics of the noise. The numerical solution of the L 1 -TV minimization problem with a spatially adapted parameter is obtained by a superlinearly convergent algorithm based on Fenchel-duality and inexact semismooth Newton techniques, which is stable with respect to noise in the data. Numerical results justifying the advantage of such a regularization parameter choice rule are presented.

Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

Abstract: Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the “ground-truth” reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.