We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a componentwise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally des...

This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a timesequence of peaks which satisfies continui...

Purpose – The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics. Design/methodology/approach – The introduced adaptive filter scheme is composed of two parallel UKFs. At every time-step, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UK...

The aim of this paper is robust identification of smart foam, as an electroacoustic transducer, considering unmodeled dynamics due to nonlinearities in behaviour at low frequencies and measurement noise at high frequencies as existent uncertainties. Set membership estimation combined with model error modelling technique is used where the approach is based on worst case scenario with unknown but...

Noise estimation is critical factor of any speech enhancement system. In presence of additive nonstationary background noise, it is difficult to understand speech for normal hearing particularly for hearing impaired person. The background interfering noise reduces the intelligibility and perceptual quality of speech. Speech enhancement with various noise estimation techniques attempts to minimi...

A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian) with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed ...

We present a sequential noise compensation method based on the sequential Kullback proximal algorithm, which uses the Kullback-Leibler divergence as a regularization function for the maximum likelihood estimation. The method is implemented as filters. In contrast to sequential noise compensation method based on the sequential EM algorithm, the convergence rate of the method and estimation error...

A noise reduction algorithm for signals corrupted by additive unknown Ls white noise is proposed using an Hm filtering framework. The proposed algorithm consists of two steps: a signal enhancement step and a parameter estimation step, which are iterated at each instant. To weaken the dependence between the signal enhancement step and the parameter estimation step, a total least squares estimati...

Currently, most noise reduction algorithms are based on an a priori information such as signal-to-noise ratio (SNR) or noise parameters estimation. They are mostly performed in the spectral domain to reduce the background noise at each frequency bin. However noise reduction in the spectral domain may introduce musical noise and artefacts which are in some cases perceptually more annoying than t...

The QUEST measurement noise model for unit vector observations has been widely used in spacecraft attitude estimation for more than twenty years. It was derived under the approximation that the noise lies in the tangent plane of the respective unit vector and is axially symmetrically distributed about the vector. For large field-of-view sensors, however, this approximation may be poor, especial...