Results 1 - 10 of 994

Low-complexity linear demosaicing using joint spatial-chromatic image statistics

by Javier Portilla - IEEE Int’l Conf on Image Processing , 2005
"... We present an efficient Linear Minimum Mean Square Error (LMMSE) method for reconstructing full color images from single sensor Color Filter Array (CFA) data. We use a representative set of full color images to estimate the joint spatial-chromatic covariance among pixel color components. Then, we de ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
mosaic and color sample. As an extension, we include blur and noise in the training process, obtaining localized mosaic-constrained Wiener estimators that partially compensate for these degradations. We show that this simple method provides an excellent trade-off between performance and computational

Image denoising by sparse 3D transform-domain collaborative filtering

by Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian - IEEE TRANS. IMAGE PROCESS , 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
Abstract - Cited by 424 (32 self) - Add to MetaCart
different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its

Two-Dimensional Pilot-Symbol-Aided Channel Estimation By Wiener Filtering

by Peter Hoeher, Stefan Kaiser, Patrick Robertson - IEEE ICASSP , 1997
"... The potentials of pilot-symbol-aided channel estimation in two dimensions are explored. In order to procure this goal, the discrete shift-variant 2-D Wiener filter is derived and analyzed given an arbitrary sampling grid, an arbitrary (but possibly optimized) selection of observations, and the possi ..."
Abstract - Cited by 140 (3 self) - Add to MetaCart
The potentials of pilot-symbol-aided channel estimation in two dimensions are explored. In order to procure this goal, the discrete shift-variant 2-D Wiener filter is derived and analyzed given an arbitrary sampling grid, an arbitrary (but possibly optimized) selection of observations

Implicit estimation of Wiener series

by Matthias O. Franz, Bernhard Schslkopf - In Proc. IEEE MLSP 2004 , 2004
"... Abstract. The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its applica-tion to high-dimensional and strongly nonl ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
Abstract. The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its applica-tion to high-dimensional and strongly

Identification of Wiener Models

by Anna Hagenblad, Anna Hagenblad - M.S. thesis, Division of Automatic Control, Department of Electrical Engineering Linkopings universitet , 1998
"... The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We d ..."
Abstract - Cited by 26 (1 self) - Add to MetaCart
The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We

Quantile based noise estimation for spectral subtraction and wiener filtering

by Volker Stahl, Er Fischer - in Proc. IEEE Int. Conf. Acoust., Speech, and Sig. Proc. (ICASSP’00 , 2000
"... Elimination of additive noise from a speech signal is a fun-damental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps: First ..."
Abstract - Cited by 55 (0 self) - Add to MetaCart
: First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averag-ing. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener ltering

Hammerstein-Wiener System Estimator Initialization

by Ph. Crama, J. Schoukens
"... In nonlinear system identification, the system is often represented as a series of blocks linked together. Such block-oriented models are built with static nonlinear subsystems and linear dynamic systems. This paper deals with the identification of the Hammerstein-Wiener model, which is a block-orie ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
In nonlinear system identification, the system is often represented as a series of blocks linked together. Such block-oriented models are built with static nonlinear subsystems and linear dynamic systems. This paper deals with the identification of the Hammerstein-Wiener model, which is a block

Maximum likelihood estimation of wiener models

by Anna Hagenblad, Lennart Ljung, Anna Hagenblad, Lennart Ljung - In Proc. 39:th IEEE Conf. on Decision and Control , 2000
"... Technical reports from the Automatic Control group in Linköping are available by anonymous ftp at the address ftp.control.isy.liu.se. This report is contained in the le 2308.pdf. ..."
Abstract - Cited by 5 (2 self) - Add to MetaCart
Technical reports from the Automatic Control group in Linköping are available by anonymous ftp at the address ftp.control.isy.liu.se. This report is contained in the le 2308.pdf.

Improved Wavelet Denoising via Empirical Wiener Filtering

by Sandeep Ghael, Eep P. Ghael, Akbar M. Sayeed, Richard G. Baraniuk - Proceedings of SPIE , 1997
"... Wavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. Wavelet shrinkage using thresholding is asymptotically optimal in a minimax mean-square error (MSE) sense over a variety of smoothness spaces. However, for any g ..."
Abstract - Cited by 51 (10 self) - Add to MetaCart
given signal, the MSE-optimal processing is achieved by the Wiener filter, which delivers substantially improved performance. In this paper, we develop a new algorithm for wavelet denoising that uses a wavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter. The shrinkage estimate

Estimation of Generalised Hammerstein-Wiener Systems ⋆

by Adrian Wills, Brett Ninness
"... Abstract: This paper examines the use of a so-called “generalised Hammerstein–Wiener ” model structure that is formed as the concatenation of an arbitrary number of Hammerstein systems. The latter are taken here to be memoryless non-linearities followed by linear time invariant dynamics. Hammerstein ..."
Abstract - Add to MetaCart
. Hammerstein, Wiener, Hammerstein–Wiener and Wiener–Hammerstein models are all special cases of this structure. The parameter estimation of this model is investigated by using a standard prediction error criterion coupled with a robust gradient based search algorithm. This approach is profiled using
Results 1 - 10 of 994