Robust High Dynamic Range Imaging by Rank Minimization
This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when st...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2015-06, Vol.37 (6), p.1219-1232 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1232 |
|---|---|
| container_issue | 6 |
| container_start_page | 1219 |
| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 37 |
| creator | Oh, Tae-Hyun Lee, Joon-Young Tai, Yu-Wing Kweon, In So |
| description | This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples. |
| doi_str_mv | 10.1109/TPAMI.2014.2361338 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_1699221936</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6915885</ieee_id><sourcerecordid>1711547955</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-af94a13e4b2ade93cb87b06289b5eab45045e30bb476c5e479f1e472f1d303de3</originalsourceid><addsrcrecordid>eNpdkE1PwkAQhjdGI4j-AU1MEy9eivvd3SPBD0ggGoLnzW67rYu0xW57wF_vIsjBy0wy87yTyQPANYJDhKB8WL6N5tMhhogOMeGIEHEC-kgSGRNG5CnoQ8RxLAQWPXDh_QoGkkFyDnqYE5YQSvuAL2rT-TaauOIjetxWunRptNBVYaNpqQtXFZHZ7gaf0dxVrnTfunV1dQnOcr329urQB-D9-Wk5nsSz15fpeDSLU8p5G-tcUo2IpQbrzEqSGpEYyLGQhlltwjeUWQKNoQlPmaWJzFGoOEcZgSSzZADu93c3Tf3VWd-q0vnUrte6snXnFUoQYiHGWEDv_qGrumuq8J1CXEqMgxkeKLyn0qb2vrG52jSu1M1WIah2VtWvVbWzqg5WQ-j2cLozpc2OkT-NAbjZA85ae1xziZgQjPwAz0h5ZA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1699221936</pqid></control><display><type>article</type><title>Robust High Dynamic Range Imaging by Rank Minimization</title><source>IEEE Electronic Library (IEL)</source><creator>Oh, Tae-Hyun ; Lee, Joon-Young ; Tai, Yu-Wing ; Kweon, In So</creator><creatorcontrib>Oh, Tae-Hyun ; Lee, Joon-Young ; Tai, Yu-Wing ; Kweon, In So</creatorcontrib><description>This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2014.2361338</identifier><identifier>PMID: 26357344</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Alignment ; Cameras ; Dynamic range ; Heuristic algorithms ; High Dynamic Range Image ; Image reconstruction ; Matrix Completion ; Minimization ; Multi-exposure fusion ; Rank minimization ; Robustness ; RPCA</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2015-06, Vol.37 (6), p.1219-1232</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jun 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-af94a13e4b2ade93cb87b06289b5eab45045e30bb476c5e479f1e472f1d303de3</citedby><cites>FETCH-LOGICAL-c466t-af94a13e4b2ade93cb87b06289b5eab45045e30bb476c5e479f1e472f1d303de3</cites><orcidid>0000-0002-3148-0380</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6915885$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27933,27934,54767</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6915885$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26357344$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oh, Tae-Hyun</creatorcontrib><creatorcontrib>Lee, Joon-Young</creatorcontrib><creatorcontrib>Tai, Yu-Wing</creatorcontrib><creatorcontrib>Kweon, In So</creatorcontrib><title>Robust High Dynamic Range Imaging by Rank Minimization</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.</description><subject>Alignment</subject><subject>Cameras</subject><subject>Dynamic range</subject><subject>Heuristic algorithms</subject><subject>High Dynamic Range Image</subject><subject>Image reconstruction</subject><subject>Matrix Completion</subject><subject>Minimization</subject><subject>Multi-exposure fusion</subject><subject>Rank minimization</subject><subject>Robustness</subject><subject>RPCA</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1PwkAQhjdGI4j-AU1MEy9eivvd3SPBD0ggGoLnzW67rYu0xW57wF_vIsjBy0wy87yTyQPANYJDhKB8WL6N5tMhhogOMeGIEHEC-kgSGRNG5CnoQ8RxLAQWPXDh_QoGkkFyDnqYE5YQSvuAL2rT-TaauOIjetxWunRptNBVYaNpqQtXFZHZ7gaf0dxVrnTfunV1dQnOcr329urQB-D9-Wk5nsSz15fpeDSLU8p5G-tcUo2IpQbrzEqSGpEYyLGQhlltwjeUWQKNoQlPmaWJzFGoOEcZgSSzZADu93c3Tf3VWd-q0vnUrte6snXnFUoQYiHGWEDv_qGrumuq8J1CXEqMgxkeKLyn0qb2vrG52jSu1M1WIah2VtWvVbWzqg5WQ-j2cLozpc2OkT-NAbjZA85ae1xziZgQjPwAz0h5ZA</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>Oh, Tae-Hyun</creator><creator>Lee, Joon-Young</creator><creator>Tai, Yu-Wing</creator><creator>Kweon, In So</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>NPM</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>7X8</scope><orcidid>https://orcid.org/0000-0002-3148-0380</orcidid></search><sort><creationdate>20150601</creationdate><title>Robust High Dynamic Range Imaging by Rank Minimization</title><author>Oh, Tae-Hyun ; Lee, Joon-Young ; Tai, Yu-Wing ; Kweon, In So</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-af94a13e4b2ade93cb87b06289b5eab45045e30bb476c5e479f1e472f1d303de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Alignment</topic><topic>Cameras</topic><topic>Dynamic range</topic><topic>Heuristic algorithms</topic><topic>High Dynamic Range Image</topic><topic>Image reconstruction</topic><topic>Matrix Completion</topic><topic>Minimization</topic><topic>Multi-exposure fusion</topic><topic>Rank minimization</topic><topic>Robustness</topic><topic>RPCA</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oh, Tae-Hyun</creatorcontrib><creatorcontrib>Lee, Joon-Young</creatorcontrib><creatorcontrib>Tai, Yu-Wing</creatorcontrib><creatorcontrib>Kweon, In So</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>PubMed</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>MEDLINE - Academic</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Oh, Tae-Hyun</au><au>Lee, Joon-Young</au><au>Tai, Yu-Wing</au><au>Kweon, In So</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust High Dynamic Range Imaging by Rank Minimization</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2015-06-01</date><risdate>2015</risdate><volume>37</volume><issue>6</issue><spage>1219</spage><epage>1232</epage><pages>1219-1232</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>26357344</pmid><doi>10.1109/TPAMI.2014.2361338</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-3148-0380</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2015-06, Vol.37 (6), p.1219-1232 |
| issn | 0162-8828 1939-3539 2160-9292 |
| language | eng |
| recordid | cdi_proquest_journals_1699221936 |
| source | IEEE Electronic Library (IEL) |
| subjects | Alignment Cameras Dynamic range Heuristic algorithms High Dynamic Range Image Image reconstruction Matrix Completion Minimization Multi-exposure fusion Rank minimization Robustness RPCA |
| title | Robust High Dynamic Range Imaging by Rank Minimization |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-30T12%3A31%3A11IST&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=Robust%20High%20Dynamic%20Range%20Imaging%20by%20Rank%20Minimization&rft.jtitle=IEEE%20transactions%20on%20pattern%20analysis%20and%20machine%20intelligence&rft.au=Oh,%20Tae-Hyun&rft.date=2015-06-01&rft.volume=37&rft.issue=6&rft.spage=1219&rft.epage=1232&rft.pages=1219-1232&rft.issn=0162-8828&rft.eissn=1939-3539&rft.coden=ITPIDJ&rft_id=info:doi/10.1109/TPAMI.2014.2361338&rft_dat=%3Cproquest_RIE%3E1711547955%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=1699221936&rft_id=info:pmid/26357344&rft_ieee_id=6915885&rfr_iscdi=true |