Impulse noise

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

Noise eliminative circuit employing a coring circuit

Abstract: A noise eliminative circuit for eliminating noise components contained in an input image signal in which a high-frequency noise component is eliminated through a coring circuit, and a low-frequency noise component is selectively eliminated by horizontal and vertical correlation, without a damage to the input image signal. A coring circuit is employed for a horizontal high-frequency component of an image signal to remove a minute noise component, and an impulse noise elimination method is applied to a remaining component of the image signal to eliminate the noise component which is also removed in accordance with a user's selection of the high-frequency band.

Robust adaptive array for wireless communications

Abstract: In the application of a receiver antenna array to wireless communications, a known signal preamble is used for estimating the propagation vector at the beginning of each data frame. The estimated propagation vector is then used in linear combining of array inputs for interference suppression and demodulation of a desired user's information data stream. Since the training preamble is usually very short, conventional training methods, which estimate the propagation vector based solely on the training preamble, may incur large estimation errors. In many wireless channels, the ambient noise is known to be decidedly non-Gaussian, due to impulsive phenomena. The conventional training methods may suffer further from such impulsive noise. Moreover, performance of linear combining techniques can degrade substantially in the presence of impulsive noise. We first propose a new technique for propagation vector estimation which exploits the whole frame of the received signal. It is shown that as the length of the signal frame tends to infinity, in the absence of noise, this method can recover the propagation vector of the desired user exactly, given a small number of training symbols for that user. We then develop robust techniques for propagation vector estimation and array combining in the presence of impulsive noise. These techniques are nonlinear in nature and are based on the M-estimation method. It is seen that the proposed robust methods offer performance improvement over linear techniques in non-Gaussian noise, with little attendant increase in computational complexity. Finally, we address the extension of the proposed techniques to dispersive channels with intersymbol interference.

An intelligent image agent based on soft-computing techniques for color image processing

Abstract: An intelligent image agent based on soft-computing techniques for color image processing is proposed in this paper. The intelligent image agent consists of a parallel fuzzy composition mechanism, a fuzzy mean related matrix process and a fuzzy adjustment process to remove impulse noise from highly corrupted images. The fuzzy mechanism embedded in the filter aims at removing impulse noise without destroying fine details and textures. A learning method based on the genetic algorithm is adopted to adjust the parameters of the filter from a set of training data. By the experimental results, the intelligent image agent achieves better performance than the state-of-the-art filters based on the criteria of Peak-Signal-to-Noise-Ratio (PSNR) and Mean-Absolute-Error (MAE). On the subjective evaluation of those filtered images, the intelligent image agent also results in a higher quality of global restoration.

A noise-exclusive adaptive filtering framework for removing impulse noise in digital images

Abstract: A class of noise-exclusive adaptive filters for removing impulse noise from digital images is developed and analyzed in this brief. The filtering scheme is based on noise detection using a self-organizing neural network and noise excluding estimation. These filters suppress impulse noise effectively while preserving fine image details. Applications of the filters to several images show that their properties of efficient impulse noise suppression, edges and fine detail preservation, minimum signal distortion, or minimum mean square error are better than those of the traditional median-type filters.

Performance of wavelet packet-division multiplexing in impulsive and Gaussian noise

Abstract: Wavelet packet-division multiplexing (WPDM) is a high-capacity, flexible, and robust multiple-signal transmission technique in which the message signals are waveform coded onto wavelet packet basis functions for transmission. We derive an expression for the probability of error for a WPDM scheme in the presence of both impulsive and Gaussian noise sources and demonstrate that WPDM can provide greater immunity to impulsive noise than both a time-division multiplexing scheme and an orthogonal frequency-division multiplexing scheme.