Impulse noise

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

Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise Using Blind Inpainting

Abstract: This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and $\ell_0$ minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods; these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.

Impulsive noise measurements and characterization in a UHF digital TV channel

Abstract: This paper presents the results of a study covering measurement and characterization of the wide-band impulsive noise present in a digital TV radio channel. Measurements were conducted at a frequency of 762 MHz in different outdoor and indoor environments using vertical and horizontal polarization. The measurement system was built on commercial equipment only. The calibration process, which is an important stage of this kind of measurements, is described. To analyze the measurements the impulsive noise has been modeled as a pulse train where the pulse amplitude, pulse duration and elapsed time between pulses are considered random variables. It has been found that the pulse duration and elapsed time between pulses is not dependent on the antenna polarization while the pulse amplitude is, especially in the case of the noise generated by a fluorescent lamp. It has also been found that the pulse duration of the noise measured in the outdoor environments presents some clustering features and is correlated with the pulse amplitudes. This correlation may be caused by a RF noise bandwidth that is larger than the bandwidth of the measurement system. The noise in busy streets presents larger pulse durations, larger amplitude, and shorter elapsed time between pulses that the noise measured in a pedestrian area. Several statistical tests have been done to find the distribution function that best fits these random variables. Power Rayleigh, lognormal, exponential, Poisson, and Gamma distributions have been tested. According to the assessment carried out, none of the distribution functions is adequate to model the pulse amplitudes or the elapsed time between pulses, while the pulse duration seems to be Gamma distributed.

Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten $p$ -Norm Minimization

Abstract: Hyperspectral images (HSIs) are inevitably corrupted by mixture noise during their acquisition process, in which various kinds of noise, e.g., Gaussian noise, impulse noise, dead lines, and stripes, may exist concurrently. In this paper, mixture noise removal is well illustrated by the task of recovering the low-rank and sparse components of a given matrix, which is constructed by stacking vectorized HSI patches from all the bands at the same position. Instead of applying a traditional nuclear norm, a nonconvex low-rank regularizer, i.e., weighted Schatten p -norm (WSN), is introduced to not only give better approximation to the original low-rank assumption but also to consider the importance of different rank components. The resulted nonconvex low-rank matrix approximation (LRMA) model falls into the applicable scope of an augmented Lagrangian method, and its WSN minimization subproblem can be efficiently solved by generalized iterated shrinkage algorithm. Moreover, the proposed model is integrated into an iterative regularization schema to produce final results, leading to a completed HSI restoration framework. Extensive experimental testing on simulated and real data shows, both qualitatively and quantitatively, that the proposed method has achieved highly competent objective performance compared with several state-of-the-art HSI restoration methods.

Robust Sampling and Reconstruction Methods for Sparse Signals in the Presence of Impulsive Noise

Abstract: Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Since noise is always present in practical data acquisition systems, sensing, and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with current reconstruction algorithms, fail to recover a close approximation of the signal. In this paper, we propose robust methods for sampling and reconstructing sparse signals in the presence of impulsive noise. To solve the problem of impulsive noise embedded in the underlying signal prior the measurement process, we propose a robust nonlinear measurement operator based on the weighed myriad estimator. In addition, we introduce a geometric optimization problem based on L 1 minimization employing a Lorentzian norm constraint on the residual error to recover sparse signals from noisy measurements. Analysis of the proposed methods show that in impulsive environments when the noise posses infinite variance we have a finite reconstruction error and furthermore these methods yield successful reconstruction of the desired signal. Simulations demonstrate that the proposed methods significantly outperform commonly employed compressed sensing sampling and reconstruction techniques in impulsive environments, while providing comparable performance in less demanding, light-tailed environments.