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Topic: Wiener filter

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Kalman filter

Autocorrelation

Norbert Wiener

	Wiener filtering
		Wiener filtering is a method to recover the original signal as close as possible from the received signal.
		Filters are commenly used to extract a desired signal from a backgroud of random noise or deterministic interference.
		Thus, the performance of the wiener filter may be evaluated by listening to signals and noise.
	www.nt.e-technik.uni-erlangen.de /~rabe/SYSTOOL/SYSTOOL2.02/HTTP/wien.htm (608 words)

	Spartanburg SC \| GoUpstate.com \| Spartanburg Herald-Journal
		In signal processing, the Wiener filter is a filter proposed by Norbert Wiener during the 1940s and published in 1949.
		The input to the Wiener filter is assumed to be a signal,
		The Wiener filter has solutions for two possible cases: the case where a causal filter is desired, and the one where a non-causal filter is acceptable.
	www.goupstate.com /apps/pbcs.dll/section?category=NEWS&template=wiki&text=Wiener_filter (850 words)

	Wiener filter
		The Wiener filter is a filter proposed by Norbert Wiener during the 1940s and published [1].
		By creating a filter that filters only on the frequency domain it is possible for the filter to pass noise.
		The input to the Wiener filter is assumed to be additive noise.
	www.xasa.com /wiki/en/wikipedia/w/wi/wiener_filter.html (340 words)

	Wiener Filtering (Site not responding. Last check: 2007-10-29)
		Wiener filtering normally requires a priori knowlegde of the power spectra of the noise and the original image.
		The parameter K of the Wiener filter is related to the low frequency aspect of the Wiener filter.
		The Wiener filter behaves as a as a bandpass filter, where the highpass filter is due to the inverse filter and the lowpass filter to the parameter K.
	www.dca.fee.unicamp.br /dipcourse/html-dip/c7/s3/front-page.html (202 words)

	NationMaster - Encyclopedia: Point estimation (Site not responding. Last check: 2007-10-29)
		Jump to: navigation, search The Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements.
		The Wiener filter is a filter proposed by Norbert Wiener during the 1940s and published [1].
		In statistics, point estimation involves the use of sample data to calculate a single value (known as a statistic) which is to serve as a "best guess" for an unknown (fixed or random) population parameter.
	www.nationmaster.com /encyclopedia/Point-estimation (839 words)

	Wiener_filter - The Wordbook Encyclopedia (Site not responding. Last check: 2007-10-29)
		The Wiener filter is a filter proposed by Norbert Wiener during the 1940s and published in 1949.
		The input to the Wiener filter is assumed to be a signal,
		The Wiener filter has solutions for two possible cases: the case where a causal filter is desired, and the one where a non-causal filter is acceptable.
	www.thewordbook.com /Wiener_filter (908 words)

	The Wiener filter
		Calculation of the Wiener filter requires the assumption that the signal and noise processes are second-order stationary (in the random process sense).
		Wiener filters are unable to reconstruct frequency components which have been degraded by noise.
		Wiener filters are comparatively slow to apply, since they require working in the frequency domain.
	homepages.inf.ed.ac.uk /rbf/CVonline/LOCAL_COPIES/VELDHUIZEN/node15.html (452 words)

	The Wiener filter
		Calculation of the Wiener filter requires the assumption that the signal and noise processes are second-order stationary (in the random process sense).
		Wiener filters are unable to reconstruct frequency components which have been degraded by noise.
		Wiener filters are comparatively slow to apply, since they require working in the frequency domain.
	ubiety.uwaterloo.ca /~tveldhui/papers/MAScThesis/node15.html (452 words)

	the optimal recovery and the Wiener filter
		So in the image reconstruction, the optimal recovery may produce better result than the Wiener filter if more prior knowledge about images is available besides the power sepectrum.
		My question is the argument that filters designed by the Chebyshev norm in the frequency domain don't interpolate known samples.
		I'm confused by the relationship between the optimal recovery and the Wiener filter.
	www.forum-one.org /new-410284-4337.html (309 words)

	The Wiener filter
		Calculation of the Wiener filter requires the assumption that the signal and noise processes are second-order stationary (in the random process sense).
		Wiener filters are unable to reconstruct frequency components which have been degraded by noise.
		Wiener filters are comparatively slow to apply, since they require working in the frequency domain.
	www.osl.iu.edu /~tveldhui/papers/MAScThesis/node15.html (452 words)

	WIENER - Applies a Wiener filter to a one- or two-dimensional array
		The Wiener filter attempts to minimise the mean squared difference between the undegraded image and the restored image.
		The filtering is done by multiplying the Fourier transform of the supplied image by the Fourier transform of the filter function.
		The convolutions required by the Wiener filter are performed by the multiplication of Fourier transforms.
	star-www.rl.ac.uk /cgi-bin/htxserver/sun95.htx/sun95.html?xref_WIENER (1451 words)

	"NG31C-1611" in fm06
		AB: Kriging, wiener filters, support vector machines (SVMs), neural networks, linear and non-linear inversion are methods for predicting the values of one set of variables given the values of another.
		Wiener filters were developed in signal processing to predict the values of one time-series from measurements of another.
		In kriging and wiener filtering, comparisons between data appear as ensemble averages based on distances in time or space.
	www.agu.org /cgi-bin/wais?hh=NG31C-1611 (611 words)

	How it works -- technical
		In this section, we discuss the characteristics of a Wiener filter in the presence of random noise and formulate performance expectations.
		This is especially important in an adaptive filter, since the tracking time must be able to keep up with the timescale of the changing statistics, i.e., the ongoing movement of the bottom of the bowl.
		In the case of interference arriving at the telescope from an over-the-horizon source, the adaptive filter performance is reduced as the telescope elevation is decreased in the direction of the interference, but the larger the telescope, the greater the tolerable elevation angle.
	www.valdosta.edu /~cbarnbau/WebHelpRFI/how_it_works_tech.htm (3122 words)

	[No title]
		These filters are applied in the frequency domain; the advantages or disadvantages of this technique as opposed to the filtering in the spatial domain that is done with 2D Filter or the 2D convolution applications are 1) For a filter which has a large spatial extent, the frequency domain techniques will be generally faster.
		The first, filter_order, is the order of the filter (valid values are integers between 1 and 16, inclusive), and the second is the cutoff frequency as a fraction of the Nyquist frequency (valid values are between 0 and 1).
		The inverse filter is computed as a simplified Wiener filter: H(k) / (H(k) H(k) + C) where H(k) is the frequency response of the filter to be inverted, H*(k) is complex conjugate of H(k), and C is a non-negative constant.
	www.msg.ucsf.edu /IVE/HELP/FFilter2D.hlp (2706 words)

	Priism Help: 3D Filter (Frequency domain)
		These filters are applied in the frequency domain; the advantages or disadvantages of this technique as opposed to the filtering in the spatial domain that is done with 3D Filter are
		A Gabor filter is a good choice for selecting the components of an image that contribute to a limited range of frequencies.
		If you specify this option, the noise suppression factor for the Wiener filter is interpreted as a multiplier for the average amplitude of the high frequency components of the input volume divided by the average amplitude for all frequency components in the input volume.
	util.ucsf.edu /IVE/IVE4_HTML/FFilter3D.html (2789 words)

	ASAP '98 A New Interpretation of the Wiener Filter - J. Scott Goldstein, I.S. Reed, and L.L. Scharf (Site not responding. Last check: 2007-10-29)
		The vector process observed by the Wiener filter is first decomposed by a sequence of transformations which project the data onto the cross-correlation vector and its orthogonal complement, in a stage-by-stage manner.
		The implementation of this Wiener filter is efficient, and can be realized in the voltage domain by a data matrix bidiagonalization technique or in the power domain using the Householder tridiagonalization.
		A narrowband array processing example is presented to compare the performance of the multistage Wiener filter, the cross-spectral metric Wiener filter and the principal-components Wiener filter as a function of rank.
	www.ll.mit.edu /asap/asap_98/abstract/11.html (223 words)

	A Plan for Spam
		This (a) makes the filters more effective, (b) lets each user decide their own precise definition of spam, and (c) perhaps best of all makes it hard for spammers to tune mails to get through the filters.
		I used to think that whitelists would make filtering easier, because you'd only have to filter email from people you'd never heard from, and someone sending you mail for the first time is constrained by convention in what they can say to you.
		To beat Bayesian filters, it would not be enough for spammers to make their emails unique or to stop using individual naughty words.
	www.paulgraham.com /spam.html (5051 words)

	Image Processing Fundamentals - Basic Enhancement and Restoration Techniques
		Within the class of linear filters, the optimal filter for restoration in the presence of noise is given by the Wiener filter.
		The Wiener filter is characterized in the Fourier domain and for additive noise that is independent of the signal it is given by:
		For this specific comparison, the Wiener filter generates a lower error than any of the other procedures that are examined here.
	www.ph.tn.tudelft.nl /Courses/FIP/noframes/fip-Basic.html (1117 words)

	ENEE 631 Homework #1
		In order to restore the degraded images using a pseudo-inverse or Wiener filter, it is necessary to estimate the frequency response of the degradation function H(u,v).
		For the linear motion filter, the angle has been set to 0 degrees and the displacement to L=23 (this is consistent with the fl bands appearing on the left and right part of the image whose length is 11 pixels).
		The Wiener filtered image shows higher ringing artifacts around the sharp intensity transitions of the image than the regularized pseudo-inverse filtered image.
	www.glue.umd.edu /~dgromero/631/Homework_3.htm (921 words)

	New Page 1
		The aim of this laboratory session is to reinforce the concepts associated with the Fourier Transform.
		For the first task of this lab, we were ask to construct a few low-pass, high-pass and high boost Butterworth filters by using the function lowpassfilter.m, highpassfilter.m and highboostfilter.m provided to us.
		When using the low order filter for the high pass filter, the image obtain is smoother compare to a high order high pass filter.
	www.angelfire.com /scary/geoffrey/vision/lab5.htm (567 words)

	LIST OF DSP & FILTER JOURNAL AND CONFERENCE PAPERS of C.S. Lindquist
		Lindquist, C.S., J.S. Flaks, and J. Sutter, ``Adaptive filters for speech,'' Intl.
		Lindquist, C.S. and C.C. Powell, ``A new signal estimation algorithm for use in Wiener filters,'' Intl.
		Taylor, A. and C.S. Lindquist, ``MFMBO filter response," IEEE Trans.
	www.lindquistsystemsgroup.com /papers.htm (2278 words)

	Robust Implementations of the Multistage Wiener Filter (ResearchIndex) (Site not responding. Last check: 2007-10-29)
		Abstract: Robust Implementations of the Multistage Wiener Filter By John David Hiemstra The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF).
		The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections.
		1 Adaptive beamforming using the multistage Wiener filter with..
	citeseer.ist.psu.edu /hiemstra03robust.html (1083 words)

	Signal Processing Laboratory
		The Wiener filter is an optimal linear filter which is obtained by minimizing the least rnean square error [6], [7].
		Although the Wiener filter has a simple mathernatical representation in the frequency domain, the spectral density functions of the signal and noise are not usually known a priori and cannot be estimated from the received time sequence using conventional spectrllm estimation.
		The purpose of this work is to statistically evaluate the performance of the multi-step SSP under two conditions: known a priori target spectral characteristics (i.e., center frequency and bandwidth) which, in turn, identifies the optimal spectral rangc for processing, and adaptively obtaining the processing frequencies using group delay moving entropy.
	cbis.ece.drexel.edu /SPL/jan1998.htm (3051 words)

	Chapter 4.4
		If Wiener filtration is used, the nature of degradation H and statistical parameters of the noise need to be known.
		Wiener filtration theory solves the problem of a posteriori linear mean square estimate -- all statistics (e.g., power spectrum) should be available in advance.
		Note that the inverse filter discussed earlier is a special case of the Wiener filter where noise is absent i.e.
	www.eng.iastate.edu /ee528/sonkamaterial/chapter_4_4.htm (1022 words)

	Wiener Filtering -- Theory
		The Wiener Filter is a noise filter based on Fourier iteration.
		There is another way to Wiener Filtering a signal but this time without Fourier Transform the data.
		The Mean-squared Methods uses the fact that the Wiener Filter is one that is based on the least-squared principle, i.e.
	www.math.tau.ac.il /~turkel/notes/wiener-theory.html (537 words)

	Wiener filter for equlization
		03 Sep 2004 16:00 Wiener filter for equlization
		Also you know that there is a famous book Adaptive Filtering, which is a famous text book in universities, I think that you can easily obtain it and use it's Equations.
		16 Sep 2004 4:14 Re: Wiener filter for equlization
	www.edaboard.com /ftopic89005.html (184 words)

	wiener_filter_ni
		After smoothing within this region by an (unweighted) median filter and subtracting smoothed version from unsmoothed, the average noise amplitude of the region is processed within
		As the Wiener filter is realized using the Fourier transform, only images with height and width fitting a power of 2 can be processed.
		- estimating the quotient of the power spectrum densities of noise and original image, - building the Wiener filter kernel with the quotient of power spectrum densities of noise and original image and with the impulse response, - processing the convolution of image and Wiener filter frequency response.
	www.halcon.de /download/documentation/reference-7.1/hdevelop/wiener_filter_ni.html (607 words)

	Software Appendix for Wiener Transfer Functions
		This appendix provides links to the IDL routines and supporting functions used to extract and use Wiener filter estimates for the transfer function.
		This appendix follows an IDL batch processing script that first extracts a Wiener filter transfer function from data collected during a measurement phase (phase 3) and then predict the behavior on a test phase (phase 4).
		Both the original E42 transfer function and the Wiener transfer function are Hermitian so the predicted behavior, while complex, actually only has real components.
	www.jsu.edu /depart/psychology/sebac/wiener (447 words)

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