IEEE-Eurasip Nonlinear Signal and Image Processing
About: IEEE-Eurasip Nonlinear Signal and Image Processing is an academic conference. The conference publishes majorly in the area(s): Noise & Noise reduction. Over the lifetime, 72 publication(s) have been published by the conference receiving 294 citation(s).
Papers published on a yearly basis
••18 May 2005
TL;DR: A three step method is proposed that posses good performances at a low computational cost for additive noise estimation and results show the performance of the proposed implementation in comparison to other approaches.
Abstract: Summary form only given. In this paper, we propose a simple and fast algorithm for additive noise estimation. In our approach, the input image is assumed to be corrupted by white additive Gaussian distributed noise. To estimate the noise variance, a three step method is proposed that posses good performances at a low computational cost. Simulations results that show the performance of the proposed implementation in comparison to other approaches are also presented in this paper.
••18 May 2005
Abstract: Summary form only given. Multi-channel and multi-spectral signals are often correlated across channels. Moreover, multispectral images have considerable similarity in in-channel correlations. But in array signal processing, due to the existence of multiple frequency components and their phase shifts, in-channel correlations may vary drastically. In this paper, a new multichannel weighted median filter is proposed which can capture the general correlation structure in array signals and process them in an efficient manner. The algorithm is further extended onto the complex domain by means of "phase coupling". The performance of the filter is presented in a three-sensor array processing example.
18 May 2005
TL;DR: This paper compares in this paper two ways how to apply some robust order statistic filter and one is to use a two-stage approach where impulses are first detected and removed, and after that additive noise is suppressed.
Abstract: Summary form only given. Images are quite often corrupted by mixed additive and impulsive noise. Then, the task in the filtering is to remove both components of the noise. We compare in this paper two ways how to do this. The first one is to apply some robust order statistic filter and the other one is to use a two-stage approach where impulses are first detected and removed, and after that additive noise is suppressed. For the latter approach, two methods are proposed. We demonstrate through experiments that the latter approach performs better than several standard order statistic filtering techniques. In addition, a modified version of a method to estimate the variance of additive noise is introduced. The given method performs well enough even with mixed noise.
18 May 2005
TL;DR: The efficiency of double-talk resilient adaptive filtering for a generalized sidelobe canceller for speech and audio signal acquisition is shown and improved robustness leads to faster convergence, to higher noise reduction, and to a better output signal quality in turn.
Abstract: Summary form only given. In adaptive filtering, undetected noise bursts often disturb the adaptation and may lead to instabilities and divergence of the adaptive filter. The sensitivity against noise bursts increases with the convergence speed of the adaptive filter and limits the performance of signal processing methods where fast convergence is required. Typical applications which are sensitive against noise bursts are adaptive beamforming for audio signal acquisition or acoustic echo cancellation, where noise bursts are frequent due to undetected double-talk. In this paper, we apply double-talk resistant adaptive filtering (Gaensler (1998)) using a nonlinear optimization criterion to adaptive beamforming in the discrete Fourier transform domain for bin-wise adaptation controls. We show the efficiency of double-talk resilient adaptive filtering for a generalized sidelobe canceller for speech and audio signal acquisition. The improved robustness leads to faster convergence, to higher noise reduction, and to a better output signal quality in turn.
18 May 2005
Abstract: Summary form only given. The problem of estimating nonstationary signals has been considered in many previous publications. In this paper we propose an alternative algorithm in order to accurately estimate AM/FM signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of polynomial approximation is limited due to the shortness of each time segment. The time support of a segment is controlled by a criterion defined on the spectrogram. To keep optimality a maximum likelihood procedure estimates the local model parameters leading to a nonlinear equation system in R7. This is solved by a simulated annealing technique. Finally, the local polynomial models are merged to reconstruct the entire signal model. The proposed algorithm enables highly nonlinear AM/FM estimation and shows robustness even when signal to noise ratio (SNR) is low. The appropriate Cramer Rao bounds (CRB) are presented for both polynomial phase and amplitude signals. Monte Carlo simulations show that the proposed algorithm performs well. Finally, our proposed method is illustrated using both numerical simulations and a real signal of whale sound.
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