• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.21-27
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• 1994

Linear combination of weighted order statistic(LWOS) filters, which is an extension of stack filters, can represent any Boolean function(BF) or its extension. Which is called the extended BF(EBF). In this paper, we present a procedure for finding an LWOS filter of the simplest type from LWOS filters which are equivalent to a given BF or EBF. In addition, a property that is useful for implementing an LWOS filter is derived and an algorithm for LWOS filtering is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.4
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• pp.639-644
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• 2002

Nonlinear filters which are utilized rank-order information and temporal-order information, have many proposed, in order to restore nonstationary signals which are corrupted by additive noise. In this paper, we propose a data-dependent LWOS filter whose coefficients change based on local statistics. LWOS(Linear Combination of Weighted Order Statistics) filters［1］which also utilized two informations, and have properties of efficient impulsive and nonimpulsive noise attenuation and sufficiently details and edges preservation. DD-LWOS filters can remove non-impulsive oises while preserving signal details. DD-LWOS2 filter gets more better performance than DD-LWOS filter when input image corrupted by additive noise which includes Impulsive noise components.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.471-478
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• 2007

The SA fillers encompass a large class of filters based on order statistics as veil as linear FIR filters. Using SA later structure, it is possible to design linear and non-linear filters under a unified framework. In this paper SA filters are applied to an image smoothing problem for mixed noise. Original image is contaminated by Gaussian and impulsive noise. Optimal SA filters are designed and applied to contaminated image. The experimental result shows that SA filters outperform linear FIR and ordering-based nonlinear filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1556-1562
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• 2003

In this paper, a new representations of extended threshold Boolean filter(ETBF), called SA filter, is introduced. The structure of this representations is a one of multistage filters. The first stage is subfilters of nonlinear filters such as maximum, minimum, or exclusive-OR operators. The second stage is linear combination of Int stage outputs. Although the structure of this representations is very similar to SAM filters, SA filters encampass all ETBF not subset of ETBF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.362-368
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• 2009

The SA filters encompass a large class of filters based on order statistics as well as linear FIR filters. The class of SA filters is defined as a multi-stage filters whose output is linear combination of nonlinear(minimum, maximum, exclusive-OR) sub-filter outputs. According to the lust stage nonlinear sub-filters, SA filters are called SAMAX, SAMIN, and SAXOR filters. In this paper, optimal SAMAX and SAMED filters are designed for images corrupted by biased noise. The performance analysis of this experiment shows that SAMAX filters outperforms SAMED filters for biased noise. In the case of un-biased noise, the SAMAX and SAMED filters give the same performance. This result leads us to a new guideline in the application of SA filters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1502-1507
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• 2005

The SA filters have a very flexible structure by limiting the maximum subwindow size. This flexible structure presents an effective trade-off between the complexity and performance of the filters. In this paper, experimental results showing the performance variation according to the change of filter order and subfilter type(such as max, min, exclusive-OR, mod) are presented. We designed optimal SA filters minimizing MSE for the various noise conditions. These results show several new properties of SA filters.