Impulse noise

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

On the Convergence of Primal–Dual Hybrid Gradient Algorithms for Total Variation Image Restoration

Abstract: In this paper we establish the convergence of a general primal–dual method for nonsmooth convex optimization problems whose structure is typical in the imaging framework, as, for example, in the Total Variation image restoration problems. When the steplength parameters are a priori selected sequences, the convergence of the scheme is proved by showing that it can be considered as an e-subgradient method on the primal formulation of the variational problem. Our scheme includes as special case the method recently proposed by Zhu and Chan for Total Variation image restoration from data degraded by Gaussian noise. Furthermore, the convergence hypotheses enable us to apply the same scheme also to other restoration problems, as the denoising and deblurring of images corrupted by Poisson noise, where the data fidelity function is defined as the generalized Kullback–Leibler divergence or the edge preserving removal of impulse noise. The numerical experience shows that the proposed scheme with a suitable choice of the steplength sequences performs well with respect to state-of-the-art methods, especially for Poisson denoising problems, and it exhibits fast initial and asymptotic convergence.

A decoding for low density parity check codes over impulsive noise channels

Abstract: Power line channel often suffers from impulsive interference generated by electrical appliances. Therefore, power line communication makes degradation due to such impulsive interference. We introduce Middleton's class a noise model into a statistical model of impulsive noise environment. In this paper, we apply LDPC (low density parity check) codes and sum-product decoding to additive white class a noise (AWAN) channels. We propose a sum-product decoding which is suitable for AWAN channels. In addition, we show the BER (bit error rate) performance of the proposed sum-product decoding in class a noise environment by computer simulation.

Iterative Interference Cancellation for OFDM Signals With Blanking Nonlinearity in Impulsive Noise Channels

Abstract: A simple iterative interference cancellation scheme for orthogonal frequency division multiplexing (OFDM) signals with blanking nonlinearity in impulsive noise channels is presented. Blanking nonlinearity has been widely used in practical OFDM systems to suppress impulsive noises at the expense of reducing signal power and generating intercarrier interference (ICI). To improve the performance of blanking nonlinearity element, we propose an iterative interference cancellation scheme which can effectively reduce the level of ICI caused by the blanking operation at the OFDM receiver. With adaptive blanking threshold for each iteration, the proposed iterative receiver design can converge to its best performance with only three iterations. Simulation results show significant performance improvement is achieved by the proposed scheme.

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise

Abstract: A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

Adaptive robust impulse noise filtering

Abstract: It is well known that when data is contaminated by non-Gaussian noise, conventional linear systems may perform poorly. The paper presents an adaptive robust filter (adaptive preprocessor) for canceling impulsive components when the nominal process (or background noise) is a correlated, possibly nonstationary, Gaussian process. The proposed preprocessor does not require iterative and/or batch processing or prior knowledge about the nominal Gaussian process; consequently, it can be implemented in real time and adapt to changes in the environment. Based on simulation results, the proposed adaptive preprocessor shows superior performances over presently available techniques for cleaning impulse noise. Using the proposed adaptive preprocessor to clean the impulsive components in received data samples, conventional linear systems based on the Gaussian assumption can work in an impulsive environment with little if any modification. The technique is applicable to a wide range of problems, such as detection, power spectral estimation, and jamming or clutter suppression in impulsive environments. >