Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization
This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. In CATV, local weights are ass...
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| Veröffentlicht in: | IEEE signal processing letters 2016-04, Vol.23 (4), p.449-453 |
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| container_title | IEEE signal processing letters |
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| creator | Kan Chang Ding, Pak Lun Kevin Baoxin Li |
| description | This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. In CATV, local weights are assigned to the residual values in the gradient domain so as to constrain the regularization strength at each pixel. In MNLR, the search of similar patches goes across different images so that both self-similarity and inter-image similarity are explored. Afterward, an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. The effectiveness of the proposed approach is demonstrated with experiments on both multiview images and video sequences. |
| doi_str_mv | 10.1109/LSP.2016.2527680 |
| format | Article |
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In CATV, local weights are assigned to the residual values in the gradient domain so as to constrain the regularization strength at each pixel. In MNLR, the search of similar patches goes across different images so that both self-similarity and inter-image similarity are explored. Afterward, an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. The effectiveness of the proposed approach is demonstrated with experiments on both multiview images and video sequences.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2016.2527680</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Compressed sensing ; Compressive sensing ; Correlation ; Detection ; Image reconstruction ; Minimization ; motion estimation/disparity estimation (ME/DE) ; nonlocal low-rank regularization (NLR) ; Reconstruction ; Regularization ; Reliability ; Signal processing algorithms ; Similarity ; total variation ; Video sequences</subject><ispartof>IEEE signal processing letters, 2016-04, Vol.23 (4), p.449-453</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c432t-e235012d6508cf6420dff8cbc98e63d8b34608cf1858622e97ad3a84d2faf593</citedby><cites>FETCH-LOGICAL-c432t-e235012d6508cf6420dff8cbc98e63d8b34608cf1858622e97ad3a84d2faf593</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7403894$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7403894$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kan Chang</creatorcontrib><creatorcontrib>Ding, Pak Lun Kevin</creatorcontrib><creatorcontrib>Baoxin Li</creatorcontrib><title>Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. In CATV, local weights are assigned to the residual values in the gradient domain so as to constrain the regularization strength at each pixel. In MNLR, the search of similar patches goes across different images so that both self-similarity and inter-image similarity are explored. Afterward, an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. The effectiveness of the proposed approach is demonstrated with experiments on both multiview images and video sequences.</description><subject>Algorithms</subject><subject>Compressed sensing</subject><subject>Compressive sensing</subject><subject>Correlation</subject><subject>Detection</subject><subject>Image reconstruction</subject><subject>Minimization</subject><subject>motion estimation/disparity estimation (ME/DE)</subject><subject>nonlocal low-rank regularization (NLR)</subject><subject>Reconstruction</subject><subject>Regularization</subject><subject>Reliability</subject><subject>Signal processing algorithms</subject><subject>Similarity</subject><subject>total variation</subject><subject>Video sequences</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAUx4MoOKd3wUvBi5fOl6RN06MUf0wGyjbPIUtfRkfbzKQV9K-3deLB03vwPt_Hlw8hlxRmlEJ-u1i9zhhQMWMpy4SEIzKhaSpjxgU9HnbIIM5zkKfkLIQdAEgq0wlZFq7Zewyh-sBohW2o2m20ROPa0PnedJVrI2ejwnmPte6wjOaN3mKI3n7IZ1e13cBv-1r76kuP_Dk5sboOePE7p2T9cL8unuLFy-O8uFvEJuGsi5HxFCgrRQrSWJEwKK2VZmNyiYKXcsMTMV6GmlIwhnmmS65lUjKrbZrzKbk5vN17995j6FRTBYN1rVt0fVBUUgEiyygd0Ot_6M71vh3KKZrJjMlEghgoOFDGuxA8WrX3VaP9p6KgRsdqcKxGx-rX8RC5OkQqRPzDswS4zBP-DRfKd9M</recordid><startdate>201604</startdate><enddate>201604</enddate><creator>Kan Chang</creator><creator>Ding, Pak Lun Kevin</creator><creator>Baoxin Li</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><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>201604</creationdate><title>Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization</title><author>Kan Chang ; Ding, Pak Lun Kevin ; Baoxin Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-e235012d6508cf6420dff8cbc98e63d8b34608cf1858622e97ad3a84d2faf593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Compressed sensing</topic><topic>Compressive sensing</topic><topic>Correlation</topic><topic>Detection</topic><topic>Image reconstruction</topic><topic>Minimization</topic><topic>motion estimation/disparity estimation (ME/DE)</topic><topic>nonlocal low-rank regularization (NLR)</topic><topic>Reconstruction</topic><topic>Regularization</topic><topic>Reliability</topic><topic>Signal processing algorithms</topic><topic>Similarity</topic><topic>total variation</topic><topic>Video sequences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kan Chang</creatorcontrib><creatorcontrib>Ding, Pak Lun Kevin</creatorcontrib><creatorcontrib>Baoxin Li</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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kan Chang</au><au>Ding, Pak Lun Kevin</au><au>Baoxin Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2016-04</date><risdate>2016</risdate><volume>23</volume><issue>4</issue><spage>449</spage><epage>453</epage><pages>449-453</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. 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| ispartof | IEEE signal processing letters, 2016-04, Vol.23 (4), p.449-453 |
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| subjects | Algorithms Compressed sensing Compressive sensing Correlation Detection Image reconstruction Minimization motion estimation/disparity estimation (ME/DE) nonlocal low-rank regularization (NLR) Reconstruction Regularization Reliability Signal processing algorithms Similarity total variation Video sequences |
| title | Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization |
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