A Wavelet-Denoising Approach Using Polynomial Threshold Operators
This letter presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft-and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal deta...
Gespeichert in:
| Veröffentlicht in: | IEEE signal processing letters 2008, Vol.15, p.906-909 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 909 |
|---|---|
| container_issue | |
| container_start_page | 906 |
| container_title | IEEE signal processing letters |
| container_volume | 15 |
| creator | Smith, C.B. Agaian, S. Akopian, D. |
| description | This letter presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft-and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal details. To avoid the complicated process of signal model identification for specific type of signals, a least squares optimization method is proposed for the polynomial coefficients. Our study shows that the proposed term-by-term, fixed-threshold operator can perform as well as adaptively applied, scale-dependent soft and hard thresholding approaches. |
| doi_str_mv | 10.1109/LSP.2008.2001815 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_863574681</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4691049</ieee_id><sourcerecordid>2330792371</sourcerecordid><originalsourceid>FETCH-LOGICAL-c290t-50d8fdd9f3d2519fa5f688a2acc8465e92de76173c47b20308743f00e9f6fc023</originalsourceid><addsrcrecordid>eNo9kFtLwzAUgIMoOKfvgi_F986TpEmTxzKvMNjADR9DTBPX0TU16YT9ezM3fDkX-M6FD6FbDBOMQT7M3hcTAiAOAQvMztAIMyZyQjk-TzWUkEsJ4hJdxbiBRGLBRqiqsg_9Y1s75I-2801suq-s6vvgtVlnq7924dt957eNbrPlOti49m2dzXsb9OBDvEYXTrfR3pzyGK2en5bT13w2f3mbVrPcEAlDzqAWrq6lozVhWDrNHBdCE22MKDizktS25Likpig_CVAQZUEdgJWOOwOEjtH9cW_67Xtn46A2fhe6dFIJTllZcIETBEfIBB9jsE71odnqsFcY1MGTSp7UwZM6eUojd8eRxlr7jxdcYigk_QXHHGMI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>863574681</pqid></control><display><type>article</type><title>A Wavelet-Denoising Approach Using Polynomial Threshold Operators</title><source>IEEE Electronic Library (IEL)</source><creator>Smith, C.B. ; Agaian, S. ; Akopian, D.</creator><creatorcontrib>Smith, C.B. ; Agaian, S. ; Akopian, D.</creatorcontrib><description>This letter presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft-and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal details. To avoid the complicated process of signal model identification for specific type of signals, a least squares optimization method is proposed for the polynomial coefficients. Our study shows that the proposed term-by-term, fixed-threshold operator can perform as well as adaptively applied, scale-dependent soft and hard thresholding approaches.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2008.2001815</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Least squares methods ; Noise reduction ; Optimization methods ; Polynomials ; Signal processing ; Wavelet coefficients ; Wavelet denoising ; Wavelet domain ; wavelet threshold operators ; Wavelet transforms</subject><ispartof>IEEE signal processing letters, 2008, Vol.15, p.906-909</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c290t-50d8fdd9f3d2519fa5f688a2acc8465e92de76173c47b20308743f00e9f6fc023</citedby><cites>FETCH-LOGICAL-c290t-50d8fdd9f3d2519fa5f688a2acc8465e92de76173c47b20308743f00e9f6fc023</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4691049$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4691049$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Smith, C.B.</creatorcontrib><creatorcontrib>Agaian, S.</creatorcontrib><creatorcontrib>Akopian, D.</creatorcontrib><title>A Wavelet-Denoising Approach Using Polynomial Threshold Operators</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>This letter presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft-and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal details. To avoid the complicated process of signal model identification for specific type of signals, a least squares optimization method is proposed for the polynomial coefficients. Our study shows that the proposed term-by-term, fixed-threshold operator can perform as well as adaptively applied, scale-dependent soft and hard thresholding approaches.</description><subject>Least squares methods</subject><subject>Noise reduction</subject><subject>Optimization methods</subject><subject>Polynomials</subject><subject>Signal processing</subject><subject>Wavelet coefficients</subject><subject>Wavelet denoising</subject><subject>Wavelet domain</subject><subject>wavelet threshold operators</subject><subject>Wavelet transforms</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFtLwzAUgIMoOKfvgi_F986TpEmTxzKvMNjADR9DTBPX0TU16YT9ezM3fDkX-M6FD6FbDBOMQT7M3hcTAiAOAQvMztAIMyZyQjk-TzWUkEsJ4hJdxbiBRGLBRqiqsg_9Y1s75I-2801suq-s6vvgtVlnq7924dt957eNbrPlOti49m2dzXsb9OBDvEYXTrfR3pzyGK2en5bT13w2f3mbVrPcEAlDzqAWrq6lozVhWDrNHBdCE22MKDizktS25Likpig_CVAQZUEdgJWOOwOEjtH9cW_67Xtn46A2fhe6dFIJTllZcIETBEfIBB9jsE71odnqsFcY1MGTSp7UwZM6eUojd8eRxlr7jxdcYigk_QXHHGMI</recordid><startdate>2008</startdate><enddate>2008</enddate><creator>Smith, C.B.</creator><creator>Agaian, S.</creator><creator>Akopian, D.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>2008</creationdate><title>A Wavelet-Denoising Approach Using Polynomial Threshold Operators</title><author>Smith, C.B. ; Agaian, S. ; Akopian, D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c290t-50d8fdd9f3d2519fa5f688a2acc8465e92de76173c47b20308743f00e9f6fc023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Least squares methods</topic><topic>Noise reduction</topic><topic>Optimization methods</topic><topic>Polynomials</topic><topic>Signal processing</topic><topic>Wavelet coefficients</topic><topic>Wavelet denoising</topic><topic>Wavelet domain</topic><topic>wavelet threshold operators</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Smith, C.B.</creatorcontrib><creatorcontrib>Agaian, S.</creatorcontrib><creatorcontrib>Akopian, D.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Smith, C.B.</au><au>Agaian, S.</au><au>Akopian, D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Wavelet-Denoising Approach Using Polynomial Threshold Operators</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2008</date><risdate>2008</risdate><volume>15</volume><spage>906</spage><epage>909</epage><pages>906-909</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>This letter presents a new class of polynomial threshold operators for denoising signals using wavelet transforms. The operators are parameterized to include classical soft-and hard-thresholding operators and have many degrees of freedom to optimally suppress undesired noise and preserve signal details. To avoid the complicated process of signal model identification for specific type of signals, a least squares optimization method is proposed for the polynomial coefficients. Our study shows that the proposed term-by-term, fixed-threshold operator can perform as well as adaptively applied, scale-dependent soft and hard thresholding approaches.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2008.2001815</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1070-9908 |
| ispartof | IEEE signal processing letters, 2008, Vol.15, p.906-909 |
| issn | 1070-9908 1558-2361 |
| language | eng |
| recordid | cdi_proquest_journals_863574681 |
| source | IEEE Electronic Library (IEL) |
| subjects | Least squares methods Noise reduction Optimization methods Polynomials Signal processing Wavelet coefficients Wavelet denoising Wavelet domain wavelet threshold operators Wavelet transforms |
| title | A Wavelet-Denoising Approach Using Polynomial Threshold Operators |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T17%3A04%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Wavelet-Denoising%20Approach%20Using%20Polynomial%20Threshold%20Operators&rft.jtitle=IEEE%20signal%20processing%20letters&rft.au=Smith,%20C.B.&rft.date=2008&rft.volume=15&rft.spage=906&rft.epage=909&rft.pages=906-909&rft.issn=1070-9908&rft.eissn=1558-2361&rft.coden=ISPLEM&rft_id=info:doi/10.1109/LSP.2008.2001815&rft_dat=%3Cproquest_RIE%3E2330792371%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=863574681&rft_id=info:pmid/&rft_ieee_id=4691049&rfr_iscdi=true |