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Search Results: 1 - 10 of 324944 matches for " S. Marchevsky "
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Fuzzy Stack Filters for Image Processing
S. Marchevsky,Cs. Stupak
Radioengineering , 1999,
Abstract: This paper is oriented to filtering by fuzzy stack filter of monochromatic images distorted with impulsive noise. Fuzzy stack filter is acquired by extension of stack filters by means of fuzzy logic. Adding some parameters to this filter, that are adjusted by neural adaptation algorithm, is obtained the new class of filters, so-called fuzzy rank-order filters. This class of filters is compared with other well known filters as stack filters and neural stack filters.
Design of Boolean LUM Smoothers through Permutation Coloring Concept
R. Lukac,S. Marchevsky
Radioengineering , 2001,
Abstract: Rank-order based LUM (lower-upper-middle) smoothers distinguishes bywide range of smoothing characteristics given by filter parameter.Thus, for the capability to achieve the best balance between noisesuppression and signal details preservation, the LUM smoothers arepreferred in smoothing applications. Thanks to threshold decompositionand stacking properties, the LUM smoothers belong to the class of stackfilters. This paper is focused to the derivation of minimal positiveBoolean function for LUM smoothers through permutation groups and acoloring concept.
A Neural LUM Smoother
R. Lukac,S. Marchevsky
Radioengineering , 2000,
Abstract: In this paper a design of neural LUM smoother is presented. The LUMsmoother distinguishes by a number of smoothing characteristics done bythe filter parameter. However, the tuning parameter for smoothing isfixed for whole image. The new method realizes adaptive control of thelevel of smoothing by neural networks. The well-known and very popularbackpropagation algorithm is used. The analysis of the proposed methodsis evaluated through subjective and objective criteria and comparedwith the traditional LUM smoother.
Adaptive Order-Statistic LMS Filters
R. Hudec,S. Marchevsky
Radioengineering , 2001,
Abstract: The LMS-based adaptive order-statistic filters are presented in thispaper. The adaptive Ll-filters as extension of the adaptive L-filterfor two-dimensional filtering of noisy greyscale images is studied too.Their adaptation properties are studied by three types of noise, theadditive white Gaussian noise, the impulsive noise or both,respectively. Moreover, the impulsive noise has the fixed noise value(Salt & Pepper noise). The problem of pixel value multiplicity anddetermination its position in the ordered input vector for adaptiveLl-filter is shown in this article. The two types of images withdifferent of image complexity are used to demonstration of the power oftime-spatial ordering.
Extension of Impulse Detectors to Spatial Dimension and their Utilization as Switch in the LMS L-SD Filter
R. Hudec,S. Marchevsky
Radioengineering , 2001,
Abstract: In this paper, one kind of adaptive LMS filters based on orderstatistics is used for two-dimensional filtration of noisy greyscaleimages degraded by mixed noise. The signal-dependent adaptive LMSL-filter (L-SD) consists of two normalized constrained adaptive LMSL-filters, because they have better convergence properties than simpleLMS algorithm. Moreover, first filter suppresses the noise inhomogeneous regions and second filter preserves the high components offiltered image. Some versions of spatial order statistic detectors weredeveloped from the impulse detectors and were employed as switchbetween output these filters.
The Methods of Design and Implementation of Stack Filters for Image Processing
M. Drutarovsky,S. Marchevsky
Radioengineering , 1995,
Abstract: This paper deals with a large class of nonlinear digital filters, the stack filters, which contain all combinations and compositions of rank order operators within a finite window. Attention is given to design and effective hardware implementation of an optimal stack filter for image processing. Presented simulation results confirm robustness of stack filters in the image restoration corrupted by impulsive noise.
BAMUD Features Demonstration by System View
L. Longauer,S. Marchevsky,D. Kocur
Radioengineering , 2004,
Abstract: Direct-sequence code-division multiple access (DS-CDMA) is afrequently used wireless technology in DS-CDMA communications. Theconventional DS-CDMA detector follows a single-user detection strategyin which each user is detected separately without regard for the otherusers. The better strategy is multi-user detection (MUD), whereinformation about multiple users is used to improve detection of eachindividual user. This paper presents an adaptive multi-user detectorconverging (for any initialization) to the minimum mean square error(MMSE) detector without requiring training sequences. This blindmulti-user detector (BAMUD) requires no more knowledge than does theconventional single-user detector. The structure of adaptive blinddetector is simulated by the system design tool SystemView. The aimfocus is to verify theoretical knowledge of BAMUD structure usinghardware-oriented PC-based model in SystemView.
Error Concealment Method Based on Motion Vector Prediction Using Particle Filters
B. Hrusovsky,J. Mochnac,S. Marchevsky
Radioengineering , 2011,
Abstract: Video transmitted over unreliable environment, such as wireless channel or in generally any network with unreliable transport protocol, is facing the losses of video packets due to network congestion and different kind of noises. The problem is becoming more important using highly effective video codecs. Visual quality degradation could propagate into subsequent frames due to redundancy elimination in order to obtain high compression ratio. Since the video stream transmission in real time is limited by transmission channel delay, it is not possible to retransmit all faulty or lost packets. It is therefore inevitable to conceal these defects. To reduce the undesirable effects of information losses, the lost data is usually estimated from the received data, which is generally known as error concealment problem. This paper discusses packet loss modeling in order to simulate losses during video transmission, packet losses analysis and their impacts on the motion vectors losses.
A New Glass of Nonlinear Filters: Microstatistic Volterra Filters
D. Kocur,M. Drutarovsky,S. Marchevsky
Radioengineering , 1996,
Abstract: In this paper a new subset of the time-invariant microstatistic filters so-called microstatistic Volterra filters are proposed. This class of nonlinear filters is based on the idea of the conventional microstatistic filter generalization by substituting Wiener filters applied in the conventional microstatistic filter structure by Volterra filters. The advantage of the microstatistic Volterra filters in comparison with the Wiener filters, Volterra filters and conventional microstatistic filters is the fact that in the case of non-Gaussian signal processing the microstatistic Volterra filters can outperform Wiener filters, Volterra filters or conventional microstatistic filters. The validity of this basic property of the microstatistic Volterra filters is verified by a number of computer experiments. The disadvantage of the microstatistic Volterra filters is their relatively high computational complexity.
Suppression of Mixed Noise in the Similar Images by Using Adaptive LMS L-filters
D. Kocur,R. Hudec,S. Marchevsky
Radioengineering , 2000,
Abstract: In this paper, several adaptive least mean squares (LMS)location-invariant filter (L-filter) modifications will be described.These filters are based on linear combination of order statistics. Theadaptive L-filters are able to adapt well to variety of noiseprobability distribution, including impulsive noise. They also performwell in the case of nonstationary signals and, therefore, they aresuitable for image processing, too. Following this L-filter property,applications of the adaptive LMS L-filters for filteringtwo-dimensional static images degraded by mixed noise consisting ofadditive Gaussian white noise and impulsive noise will be presented inthis paper. Based on conveniently selected experiments intent on imagefiltering, the properties of a several adaptive L-filters modificationswill be demonstrated and compared. It will follow from experimentresults, that the L-filter modification called signal-dependent LMSL-filter yields the best results.
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