A new algorithm is presented for adaptive comb filtering and parametric spectral estimation of harmonic signals with additive white noise. The algorithm is composed of two cascaded parts. The first estimates the fundamental frequency and enhances the harmonic component in the input, and the second estimates the harmonic amplitudes and phases. Performance analysis provides new results for the as...

Image noise ltering has been widely perceived as an estimation problem in the spatial domain. In this paper, we deal with it as an estimation problem in an uncorrelated transform domain. This idea leads to a generalization of the adaptive LMMSE estimator for ltering noisy images. In our proposed method, the transform-domain local statistics obtained from the noisy image are exploited. Due to th...

In estimating the unknown location of a rectangular signal observed with white noise, the asymptotic risks of three important estimators are compared under L1/L2 losses. A different numerical scheme is used to improve the accuracy of Ibragimov/Hasminskii’s result, which also leads to further information and numerical comparisons about the problem.

In this paper, we present an implementation of the PARIGI method that addresses the problem of the restoration of images affected by impulse noise or by a mixture of Gaussian and impulse noise. The method relies on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are performed in the...

This paper provides a theoretical analysis of the properties of Wavelet based maximum likelihood estimation of the parameters describing 1=f processes embedded in white noise. This analysis shows that such a scheme is only consistent for spectral exponents in the range 2 (0; 1). This is in contradiction to the results suggested in previous empirical studies. When 2 (0; 1) this paper also establ...

| Image noise ltering has been widely perceived as an estimation problem in the spatial domain. We deal with it as an estimation problem in an uncorrelated transform domain. This idea leads to a generalization of the adaptive LMMSE estimator for ltering noisy images. In our proposed method, the transform-domain local statistics obtained from the noisy image are exploited. Due to the fact that t...

Periodogram ordinates of a Gaussian white-noise computed at Fourier frequencies are well known to form an i.i.d. sequence. This is no longer true in the non-Gaussian case. In this paper, we develop a full theory for weighted sums of non-linear functionals of the periodogram of an i.i.d. sequence. We prove that these sums are asymptotically Gaussian under conditions very close to those which are...

A strategy for filtering (or denoising) of high-pass signals embedded in an impulse (heavy tail) noise environments based on the robust DFT forms has been proposed recently. In this paper we investigate various approaches for design of the robust DFT used in this application. We consider the DFT estimates based on the: linear combination of order statistics (L-estimate), myriad, and Wilcoxon es...

Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of which however require user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Prev...

The estimation of the amplitude of a sinewave using traditional sine fitting algorithms which are based on square error minimization is biased in the presence of additive noise contrary to what happens generally in linear regression problems. An approximate closed form expression for the estimation error as a function of sinewave amplitude, additive noise standard deviation and number of data p...