A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring: Doc 1111

In this paper, A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted [InlineEquation not available: see fulltex...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of inequalities and applications 2014-06, Vol.2014, p.1
Hauptverfasser: Qiao, Tiantian, Wu, Boying, Li, Weiguo, Dong, Alun
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 1
container_title Journal of inequalities and applications
container_volume 2014
creator Qiao, Tiantian
Wu, Boying
Li, Weiguo
Dong, Alun
description In this paper, A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted [InlineEquation not available: see fulltext.] minimization problem [InlineEquation not available: see fulltext.]. In the computing process, the effective using of signal information can make up the detailed features of image, which may be lost in the deblurring process. Numerical experiments confirm that the new reweighted algorithm for image restoration is effective and competitive to the recent state-of-the-art algorithms.[PUBLICATION ABSTRACT]
doi_str_mv 10.1186/1029-242X-2014-238
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_1612438793</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3462551561</sourcerecordid><originalsourceid>FETCH-proquest_journals_16124387933</originalsourceid><addsrcrecordid>eNqNik1LQkEUhgcpyKw_0OpA67H5Usd2IYbtWwQSMuK513OZO5PzodGvLypat3penvdh7EaKsZR2eieFmnNl1AtXQhqutB2w4Z88-94TPrHaXLDLnDshlNTWDFn3AAFPkPCE1O4L7mD9FDwFXB6qKxQDhFjAHR15t_V4DxkRmup9wfcyfoWeAvX08ZM638ZEZd9DExNQ71qEHW59TYlCe8XOG-czXv9yxG4fl8-LFX9L8VAxl00Xawpf10ZOpTLazuZa_6_6BKqKT-Y</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1612438793</pqid></control><display><type>article</type><title>A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring: Doc 1111</title><source>SpringerLink Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Springer Nature OA Free Journals</source><creator>Qiao, Tiantian ; Wu, Boying ; Li, Weiguo ; Dong, Alun</creator><creatorcontrib>Qiao, Tiantian ; Wu, Boying ; Li, Weiguo ; Dong, Alun</creatorcontrib><description>In this paper, A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted [InlineEquation not available: see fulltext.] minimization problem [InlineEquation not available: see fulltext.]. In the computing process, the effective using of signal information can make up the detailed features of image, which may be lost in the deblurring process. Numerical experiments confirm that the new reweighted algorithm for image restoration is effective and competitive to the recent state-of-the-art algorithms.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 1025-5834</identifier><identifier>EISSN: 1029-242X</identifier><identifier>DOI: 10.1186/1029-242X-2014-238</identifier><language>eng</language><publisher>Heidelberg: Springer Nature B.V</publisher><subject>Mathematics</subject><ispartof>Journal of inequalities and applications, 2014-06, Vol.2014, p.1</ispartof><rights>The Author(s) 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Qiao, Tiantian</creatorcontrib><creatorcontrib>Wu, Boying</creatorcontrib><creatorcontrib>Li, Weiguo</creatorcontrib><creatorcontrib>Dong, Alun</creatorcontrib><title>A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring: Doc 1111</title><title>Journal of inequalities and applications</title><description>In this paper, A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted [InlineEquation not available: see fulltext.] minimization problem [InlineEquation not available: see fulltext.]. In the computing process, the effective using of signal information can make up the detailed features of image, which may be lost in the deblurring process. Numerical experiments confirm that the new reweighted algorithm for image restoration is effective and competitive to the recent state-of-the-art algorithms.[PUBLICATION ABSTRACT]</description><subject>Mathematics</subject><issn>1025-5834</issn><issn>1029-242X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNik1LQkEUhgcpyKw_0OpA67H5Usd2IYbtWwQSMuK513OZO5PzodGvLypat3penvdh7EaKsZR2eieFmnNl1AtXQhqutB2w4Z88-94TPrHaXLDLnDshlNTWDFn3AAFPkPCE1O4L7mD9FDwFXB6qKxQDhFjAHR15t_V4DxkRmup9wfcyfoWeAvX08ZM638ZEZd9DExNQ71qEHW59TYlCe8XOG-czXv9yxG4fl8-LFX9L8VAxl00Xawpf10ZOpTLazuZa_6_6BKqKT-Y</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Qiao, Tiantian</creator><creator>Wu, Boying</creator><creator>Li, Weiguo</creator><creator>Dong, Alun</creator><general>Springer Nature B.V</general><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20140601</creationdate><title>A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring</title><author>Qiao, Tiantian ; Wu, Boying ; Li, Weiguo ; Dong, Alun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_16124387933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Mathematics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qiao, Tiantian</creatorcontrib><creatorcontrib>Wu, Boying</creatorcontrib><creatorcontrib>Li, Weiguo</creatorcontrib><creatorcontrib>Dong, Alun</creatorcontrib><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Journal of inequalities and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qiao, Tiantian</au><au>Wu, Boying</au><au>Li, Weiguo</au><au>Dong, Alun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring: Doc 1111</atitle><jtitle>Journal of inequalities and applications</jtitle><date>2014-06-01</date><risdate>2014</risdate><volume>2014</volume><spage>1</spage><pages>1-</pages><issn>1025-5834</issn><eissn>1029-242X</eissn><abstract>In this paper, A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring is proposed. The algorithm is based on a generalized inverse iteration and linearized Bregman iteration, which is used for the weighted [InlineEquation not available: see fulltext.] minimization problem [InlineEquation not available: see fulltext.]. In the computing process, the effective using of signal information can make up the detailed features of image, which may be lost in the deblurring process. Numerical experiments confirm that the new reweighted algorithm for image restoration is effective and competitive to the recent state-of-the-art algorithms.[PUBLICATION ABSTRACT]</abstract><cop>Heidelberg</cop><pub>Springer Nature B.V</pub><doi>10.1186/1029-242X-2014-238</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1025-5834
ispartof Journal of inequalities and applications, 2014-06, Vol.2014, p.1
issn 1025-5834
1029-242X
language eng
recordid cdi_proquest_journals_1612438793
source SpringerLink Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; Springer Nature OA Free Journals
subjects Mathematics
title A new reweighted [InlineEquation not available: see fulltext.] minimization algorithm for image deblurring: Doc 1111
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T07%3A32%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new%20reweighted%20%5BInlineEquation%20not%20available:%20see%20fulltext.%5D%20minimization%20algorithm%20for%20image%20deblurring:%20Doc%201111&rft.jtitle=Journal%20of%20inequalities%20and%20applications&rft.au=Qiao,%20Tiantian&rft.date=2014-06-01&rft.volume=2014&rft.spage=1&rft.pages=1-&rft.issn=1025-5834&rft.eissn=1029-242X&rft_id=info:doi/10.1186/1029-242X-2014-238&rft_dat=%3Cproquest%3E3462551561%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1612438793&rft_id=info:pmid/&rfr_iscdi=true