Stack filters, stack smoothers, and mirrored threshold decomposition
Stack smoothers have received considerable attention in signal processing in the past decade. Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advanta...
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| Veröffentlicht in: | IEEE transactions on signal processing 1999-10, Vol.47 (10), p.2757-2767 |
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| container_title | IEEE transactions on signal processing |
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| creator | Paredes, J.L. Arce, G.R. |
| description | Stack smoothers have received considerable attention in signal processing in the past decade. Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advantages over traditional linear FIR filters, they are in essence smoothers lacking the flexibility to adequately address a number of signal processing problems that require bandpass or highpass filtering characteristics. In this paper, mirrored threshold decomposition is introduced, which, together with the associated binary PBF, define the significantly richer class of stack filters. Using threshold logic representation, a number of properties of stack filters are derived. Notably, stack filters defined in the binary domain of mirrored threshold decomposition require the use of double weighting of each sample in the integer domain. The class of recursive stack filters and the corresponding recursive weighted median (RWM) filters in the integer domain admitting negative weights are introduced. The new stack filter formulation leads to a more powerful class of estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior stack smoother structures. |
| doi_str_mv | 10.1109/78.790657 |
| format | Article |
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Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advantages over traditional linear FIR filters, they are in essence smoothers lacking the flexibility to adequately address a number of signal processing problems that require bandpass or highpass filtering characteristics. In this paper, mirrored threshold decomposition is introduced, which, together with the associated binary PBF, define the significantly richer class of stack filters. Using threshold logic representation, a number of properties of stack filters are derived. Notably, stack filters defined in the binary domain of mirrored threshold decomposition require the use of double weighting of each sample in the integer domain. The class of recursive stack filters and the corresponding recursive weighted median (RWM) filters in the integer domain admitting negative weights are introduced. 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Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advantages over traditional linear FIR filters, they are in essence smoothers lacking the flexibility to adequately address a number of signal processing problems that require bandpass or highpass filtering characteristics. In this paper, mirrored threshold decomposition is introduced, which, together with the associated binary PBF, define the significantly richer class of stack filters. Using threshold logic representation, a number of properties of stack filters are derived. Notably, stack filters defined in the binary domain of mirrored threshold decomposition require the use of double weighting of each sample in the integer domain. The class of recursive stack filters and the corresponding recursive weighted median (RWM) filters in the integer domain admitting negative weights are introduced. The new stack filter formulation leads to a more powerful class of estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior stack smoother structures.</description><subject>Applied sciences</subject><subject>Band pass filters</subject><subject>Boolean functions</subject><subject>Decomposition</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Filtering</subject><subject>Filtration</subject><subject>Finite impulse response filter</subject><subject>Information, signal and communications theory</subject><subject>Integers</subject><subject>Laboratories</subject><subject>Logic</subject><subject>Nonlinear filters</subject><subject>Recursive</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal synthesis</subject><subject>Signal, noise</subject><subject>Stacks</subject><subject>Statistics</subject><subject>Telecommunications and information theory</subject><subject>Thresholds</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0E1LxDAQBuAiCurqwaunHkQR7Jo0n3OU9RMWPKjgraTplI22zZp0D_5763bRm56STJ55GSZJjiiZUkrgUumpAiKF2kr2KHCaEa7k9nAngmVCq9fdZD_GN0Io5yD3kuun3tj3tHZNjyFepHH9jK33_WJdMF2Vti4EH7BK-0XAuPBNlVZofbv00fXOdwfJTm2aiIebc5K83N48z-6z-ePdw-xqnlkmoc-0FiU3kpqcMI1aoSqRkFxXyEujc5aTqjYcRM0M5QysUVjlvAZFjCk1VWySnI25y-A_Vhj7onXRYtOYDv0qFkABOBMgB3n6p8y1gGEI-B8qqoATNsDzEdrgYwxYF8vgWhM-C0qK79UXShfj6gd7sgk10ZqmDqazLv42gJAy5wM7HplDxJ_fTcYXxeuKjw</recordid><startdate>19991001</startdate><enddate>19991001</enddate><creator>Paredes, J.L.</creator><creator>Arce, G.R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>7SC</scope><scope>JQ2</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>19991001</creationdate><title>Stack filters, stack smoothers, and mirrored threshold decomposition</title><author>Paredes, J.L. ; Arce, G.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-885b4a61a2038e87e7be0028de4ba82320dfa495f3a1439ca7ed24f970aab8173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Applied sciences</topic><topic>Band pass filters</topic><topic>Boolean functions</topic><topic>Decomposition</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>Filtering</topic><topic>Filtration</topic><topic>Finite impulse response filter</topic><topic>Information, signal and communications theory</topic><topic>Integers</topic><topic>Laboratories</topic><topic>Logic</topic><topic>Nonlinear filters</topic><topic>Recursive</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal synthesis</topic><topic>Signal, noise</topic><topic>Stacks</topic><topic>Statistics</topic><topic>Telecommunications and information theory</topic><topic>Thresholds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Paredes, J.L.</creatorcontrib><creatorcontrib>Arce, G.R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Paredes, J.L.</au><au>Arce, G.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stack filters, stack smoothers, and mirrored threshold decomposition</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>1999-10-01</date><risdate>1999</risdate><volume>47</volume><issue>10</issue><spage>2757</spage><epage>2767</epage><pages>2757-2767</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>Stack smoothers have received considerable attention in signal processing in the past decade. Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advantages over traditional linear FIR filters, they are in essence smoothers lacking the flexibility to adequately address a number of signal processing problems that require bandpass or highpass filtering characteristics. In this paper, mirrored threshold decomposition is introduced, which, together with the associated binary PBF, define the significantly richer class of stack filters. Using threshold logic representation, a number of properties of stack filters are derived. Notably, stack filters defined in the binary domain of mirrored threshold decomposition require the use of double weighting of each sample in the integer domain. The class of recursive stack filters and the corresponding recursive weighted median (RWM) filters in the integer domain admitting negative weights are introduced. The new stack filter formulation leads to a more powerful class of estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior stack smoother structures.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/78.790657</doi><tpages>11</tpages></addata></record> |
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| subjects | Applied sciences Band pass filters Boolean functions Decomposition Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering Filtration Finite impulse response filter Information, signal and communications theory Integers Laboratories Logic Nonlinear filters Recursive Signal and communications theory Signal processing Signal synthesis Signal, noise Stacks Statistics Telecommunications and information theory Thresholds |
| title | Stack filters, stack smoothers, and mirrored threshold decomposition |
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