Online Rain/Snow Removal from Surveillance Videos
Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/sno...
Saved in:
| Published in: | IEEE transactions on image processing Vol. 30; p. 1 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM) algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC matlab 2020. |
|---|---|
| AbstractList | Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM) algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC matlab 2020. Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC_matlab_2020.Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC_matlab_2020. Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time, and those videos also include occasionally transformed background scenes and background motions caused by waving leaves or water surfaces. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well distinguish the components of background motion from rain/snow layer. The real-time ameliorated parameters in the model well encodes their temporally dynamic configurations. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves best performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. The code page is at: https://github.com/MinghanLi/OTMSCSC_matlab_2020 . |
| Author | Meng, Deyu Zhang, Lei Zhao, Qian Li, Minghan Cao, Xiangyong |
| Author_xml | – sequence: 1 givenname: Minghan surname: Li fullname: Li, Minghan organization: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong – sequence: 2 givenname: Xiangyong surname: Cao fullname: Cao, Xiangyong organization: School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi'an Jiaotong University, Shaanxi, P.R. China – sequence: 3 givenname: Qian surname: Zhao fullname: Zhao, Qian organization: School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi'an Jiaotong University, Shaanxi, P.R. China – sequence: 4 givenname: Lei surname: Zhang fullname: Zhang, Lei organization: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong – sequence: 5 givenname: Deyu surname: Meng fullname: Meng, Deyu organization: Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau, and School of Mathematics and Statistics, Xi'an Jiaotong University, Xian, Shaan'xi, PR China. (e-mail: dymeng@mail.xjtu.edu.cn) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33444139$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kEtLAzEURoNUtFX3giADbtxMvXlMMlmK-CgUKr62Q5rJQGQmqcmM4r83pdVFF67uXZzv3o8zQSPnnUHoFMMUY5BXL7PHKQGCpxQKoJjuoTGWDOcAjIzSDoXIBWbyEE1ifAfArMD8AB1SyhjDVI4RXrjWOpM9Keuunp3_yp5M5z9VmzXBd9nzED6NbVvltMnebG18PEb7jWqjOdnOI_R6d_ty85DPF_ezm-t5rikTfa4bQWvSMCKkZJrXUNNmqWjDtGJQ6CVXvJaK80JiUIUudVOr1K42nAtSakqP0OXm7ir4j8HEvups1GbdxfghVoSJspAlFiShFzvoux-CS-0SVULJGQORqPMtNSw7U1erYDsVvqtfGQmADaCDjzGY5g_BUK19V8l3tfZdbX2nCN-JaNur3nrXB2Xb_4Jnm6A1xvz9kZQwWQr6A6RpigI |
| CODEN | IIPRE4 |
| CitedBy_id | crossref_primary_10_1049_ipr2_13047 crossref_primary_10_1007_s12559_025_10435_z crossref_primary_10_1109_TIP_2024_3501855 crossref_primary_10_1016_j_envsoft_2025_106496 crossref_primary_10_1016_j_neucom_2025_131431 crossref_primary_10_1109_LSP_2021_3135196 crossref_primary_10_1016_j_cam_2023_115431 crossref_primary_10_1007_s11227_025_07603_1 crossref_primary_10_1109_ACCESS_2021_3073127 crossref_primary_10_1109_TCSVT_2022_3233655 crossref_primary_10_3390_rs13101922 crossref_primary_10_1016_j_displa_2024_102733 crossref_primary_10_1109_LSP_2021_3134171 crossref_primary_10_1007_s11263_024_02148_x crossref_primary_10_3390_app15084325 crossref_primary_10_3390_rs16213979 crossref_primary_10_3390_jmse11112065 crossref_primary_10_1109_TVCG_2022_3174656 crossref_primary_10_3390_futuretransp5020039 crossref_primary_10_1109_TITS_2023_3346473 crossref_primary_10_3233_XST_230094 crossref_primary_10_1038_s41598_024_66090_1 crossref_primary_10_1109_ACCESS_2021_3136551 crossref_primary_10_3390_fractalfract7010024 crossref_primary_10_1109_TIP_2023_3234692 crossref_primary_10_3390_s21227610 crossref_primary_10_1016_j_patcog_2022_108822 crossref_primary_10_3390_mi15020217 |
| Cites_doi | 10.1109/TIP.2003.819861 10.1109/ICCV.2017.275 10.1561/2200000016 10.1109/CVPR.2017.186 10.1007/s11263-008-0200-2 10.1109/TPAMI.2004.1262177 10.1109/34.730558 10.1109/34.969114 10.1109/TIP.2017.2708502 10.1109/CVPR.2005.177 10.1109/CVPR.2019.00176 10.1109/CVPR.2010.5539926 10.1109/ICCV.2005.253 10.1109/CVPRW.2012.6238919 10.1109/TIP.2015.2428933 10.1109/TPAMI.2012.132 10.1109/ICCV.2013.169 10.1109/ICCV.2013.247 10.1109/TIP.2018.2806202 10.1007/s11263-012-0515-x 10.1007/s11263-006-0028-6 10.1109/CVPR.2019.00739 10.1109/TIP.2016.2593343 10.1109/CVPR.2004.1315077 10.1016/j.cviu.2016.02.009 10.1109/CVPR.2017.303 10.1109/ICCV.2017.189 10.1109/CVPR.2016.299 10.1109/ICME.2006.262572 10.1007/s11042-015-2657-7 10.1109/CVPR42600.2020.00179 10.1109/TPAMI.2017.2732350 10.1109/CVPR.2018.00658 10.1109/CVPR.2019.00173 10.1109/TIP.2017.2691802 10.1109/CVPR.2017.183 10.1016/j.patcog.2011.07.009 10.1109/TIP.2011.2179057 10.1007/s11263-014-0759-8 10.1109/CVPR.2018.00695 10.2200/S00601ED1V01Y201410IVM016 10.1145/1970392.1970395 10.1109/CVPR.2017.301 10.1109/ICCV.2015.388 10.1109/CVPR.2018.00341 10.1109/CVPR42600.2020.00317 10.1109/CVPR.2010.5539957 10.1109/ICCV.2019.00529 10.1109/CVPR.2016.567 10.1109/ICASSP.2014.6854992 10.1109/TPAMI.2019.2895793 10.1109/TIP.2005.859378 10.1109/WACV.2017.145 10.1109/TCYB.2017.2677944 10.4103/0377-2063.78382 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TIP.2021.3050313 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Technology Research Database PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering |
| EISSN | 1941-0042 |
| EndPage | 1 |
| ExternalDocumentID | 33444139 10_1109_TIP_2021_3050313 9324987 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: China NSFC project grantid: 11690011; 61721002; 62076196; U1811461 – fundername: Hong Kong RGC GRF grant grantid: PolyU 152216/18E |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 53G 5VS AAYXX ABFSI AETIX AGSQL AI. AIBXA ALLEH CITATION E.L EJD H~9 ICLAB IFJZH VH1 AAYOK NPM PKN RIG Z5M 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c347t-cf73d2f427994c6d0d3fba3f4ca405cb6a6d9a665910a5c8cfda451de66728c33 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 42 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000612145300003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1057-7149 1941-0042 |
| IngestDate | Sat Sep 27 19:58:29 EDT 2025 Mon Jun 30 10:19:58 EDT 2025 Wed Feb 19 02:30:04 EST 2025 Sat Nov 29 03:21:13 EST 2025 Tue Nov 18 22:18:53 EST 2025 Wed Aug 27 06:01:51 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c347t-cf73d2f427994c6d0d3fba3f4ca405cb6a6d9a665910a5c8cfda451de66728c33 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0055-9261 0000-0001-7912-3457 0000-0002-1294-8283 0000-0002-2078-4215 0000-0001-9956-0064 |
| PMID | 33444139 |
| PQID | 2480864407 |
| PQPubID | 85429 |
| PageCount | 1 |
| ParticipantIDs | ieee_primary_9324987 crossref_primary_10_1109_TIP_2021_3050313 proquest_miscellaneous_2478598172 pubmed_primary_33444139 proquest_journals_2480864407 crossref_citationtrail_10_1109_TIP_2021_3050313 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-01-01 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – month: 01 year: 2021 text: 2021-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on image processing |
| PublicationTitleAbbrev | TIP |
| PublicationTitleAlternate | IEEE Trans Image Process |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 ref13 ref56 ref12 ref15 ref58 ref14 ref52 ref55 ref11 ref54 ref10 mairal (ref59) 2010; 11 ref17 ref16 ref19 ref18 ref51 lin (ref62) 2014 ref50 barnum (ref8) 2007 ref46 ref45 ref47 ref42 ref41 ref43 ref7 ref9 ref4 ref3 ref6 ref5 wang (ref53) 2014 ref40 ref35 ref37 zhang (ref34) 2017 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 zhang (ref44) 2013 ref24 ref23 zhao (ref49) 2014 ref26 ref25 ref20 ref63 ref22 ref21 zhao (ref48) 2015; 26 ref28 ref27 ref29 ref60 ref61 |
| References_xml | – ident: ref61 doi: 10.1109/TIP.2003.819861 – ident: ref12 doi: 10.1109/ICCV.2017.275 – ident: ref45 doi: 10.1561/2200000016 – ident: ref33 doi: 10.1109/CVPR.2017.186 – ident: ref20 doi: 10.1007/s11263-008-0200-2 – volume: 11 start-page: 19 year: 2010 ident: ref59 article-title: Online learning for matrix factorization and sparse coding publication-title: J Mach Learn Res – volume: 26 start-page: 829 year: 2015 ident: ref48 article-title: $L_{1}$ -norm low-rank matrix factorization by variational Bayesian method publication-title: IEEE Trans Neural Netw Learn Syst – ident: ref57 doi: 10.1109/TPAMI.2004.1262177 – ident: ref4 doi: 10.1109/34.730558 – ident: ref56 doi: 10.1109/34.969114 – ident: ref29 doi: 10.1109/TIP.2017.2708502 – ident: ref1 doi: 10.1109/CVPR.2005.177 – ident: ref14 doi: 10.1109/CVPR.2019.00176 – ident: ref2 doi: 10.1109/CVPR.2010.5539926 – ident: ref6 doi: 10.1109/ICCV.2005.253 – ident: ref17 doi: 10.1109/CVPRW.2012.6238919 – ident: ref22 doi: 10.1109/TIP.2015.2428933 – ident: ref54 doi: 10.1109/TPAMI.2012.132 – ident: ref47 doi: 10.1109/ICCV.2013.169 – ident: ref10 doi: 10.1109/ICCV.2013.247 – ident: ref36 doi: 10.1109/TIP.2018.2806202 – ident: ref43 doi: 10.1007/s11263-012-0515-x – start-page: 235 year: 2014 ident: ref53 article-title: A highly scalable parallel algorithm for isotropic total variation models publication-title: Proc Int Conf Mach Learn – ident: ref19 doi: 10.1007/s11263-006-0028-6 – ident: ref42 doi: 10.1109/CVPR.2019.00739 – ident: ref50 doi: 10.1109/TIP.2016.2593343 – ident: ref5 doi: 10.1109/CVPR.2004.1315077 – ident: ref55 doi: 10.1016/j.cviu.2016.02.009 – start-page: 55 year: 2014 ident: ref49 article-title: Robust principal component analysis with complex noise publication-title: Proc Int Conf Mach Learn – ident: ref11 doi: 10.1109/CVPR.2017.303 – ident: ref30 doi: 10.1109/ICCV.2017.189 – ident: ref28 doi: 10.1109/CVPR.2016.299 – ident: ref7 doi: 10.1109/ICME.2006.262572 – ident: ref27 doi: 10.1007/s11042-015-2657-7 – ident: ref24 doi: 10.1109/CVPR42600.2020.00179 – ident: ref40 doi: 10.1109/TPAMI.2017.2732350 – ident: ref15 doi: 10.1109/CVPR.2018.00658 – ident: ref38 doi: 10.1109/CVPR.2019.00173 – ident: ref32 doi: 10.1109/TIP.2017.2691802 – ident: ref35 doi: 10.1109/CVPR.2017.183 – ident: ref52 doi: 10.1016/j.patcog.2011.07.009 – ident: ref25 doi: 10.1109/TIP.2011.2179057 – ident: ref23 doi: 10.1007/s11263-014-0759-8 – ident: ref16 doi: 10.1109/CVPR.2018.00695 – ident: ref3 doi: 10.2200/S00601ED1V01Y201410IVM016 – ident: ref46 doi: 10.1145/1970392.1970395 – start-page: 1637 year: 2013 ident: ref44 article-title: Simultaneous rectification and alignment via robust recovery of low-rank tensors publication-title: Proc Adv Neural Inf Process Syst – start-page: 740 year: 2014 ident: ref62 article-title: Microsoft COCO: Common objects in context publication-title: Proc Eur Conf Comput Vis – ident: ref21 doi: 10.1109/CVPR.2017.301 – ident: ref26 doi: 10.1109/ICCV.2015.388 – ident: ref13 doi: 10.1109/CVPR.2018.00341 – ident: ref39 doi: 10.1109/CVPR42600.2020.00317 – year: 2007 ident: ref8 article-title: Spatio-temporal frequency analysis for removing rain and snow from videos publication-title: Photometric Anal Comput Vis – ident: ref51 doi: 10.1109/CVPR.2010.5539957 – ident: ref18 doi: 10.1109/ICCV.2019.00529 – year: 2017 ident: ref34 article-title: Image de-raining using a conditional generative adversarial network publication-title: arXiv 1701 05957 – ident: ref41 doi: 10.1109/CVPR.2016.567 – ident: ref58 doi: 10.1109/ICASSP.2014.6854992 – ident: ref37 doi: 10.1109/TPAMI.2019.2895793 – ident: ref60 doi: 10.1109/TIP.2005.859378 – ident: ref31 doi: 10.1109/WACV.2017.145 – ident: ref63 doi: 10.1109/TCYB.2017.2677944 – ident: ref9 doi: 10.4103/0377-2063.78382 |
| SSID | ssj0014516 |
| Score | 2.5525973 |
| Snippet | Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1 |
| SubjectTerms | alignment method Computer vision Convolutional codes convolutional sparse coding dynamic background Dynamics multi-scale online learning Rain rain/snow removal Real time Snow Snow removal Surveillance Task analysis Video Videos |
| Title | Online Rain/Snow Removal from Surveillance Videos |
| URI | https://ieeexplore.ieee.org/document/9324987 https://www.ncbi.nlm.nih.gov/pubmed/33444139 https://www.proquest.com/docview/2480864407 https://www.proquest.com/docview/2478598172 |
| Volume | 30 |
| WOSCitedRecordID | wos000612145300003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 1941-0042 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014516 issn: 1057-7149 databaseCode: RIE dateStart: 19920101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB6S0EN7SNo8WrdpcCGXQpxd661jCQ0tlBCapOzNyNIIAqld9pH8_Y5sr8mhCfRmsCSLeaBvNJ75AI4RS0anCCu8rWUhfCkL61EVtVDRIgvaqa5Q-Ie-uDCzmb3cgJOxFgYRu5_P8DQ9drn80PpVuiqb0JqCYuRN2NRa9bVaY8YgEc52mU2pC02wf52SnNrJ9fdLCgRZecpT85MyUedwLggHJIbwR6dRR6_yNNLsTpzznf_b62vYHpBl_qU3hTewgc0u7AwoMx98eLELrx61INyDsu81mqc8z-SqaR_yn_i7JfPLU-FJfrWa32MiJqK5-a_bgO1iH27Ov16ffSsGGoXCc6GXhY-aBxYF09YKr8I08Fg7HoV3hNZ8rZwK1iklCTk46Y2PwZE0AyqlmfGcH8BW0zb4DvKax8Rg5eupDoKhMlJHV0oTXaTQUbIMJmtxVn7oMZ6oLu6qLtaY2op0USVdVIMuMvg8zvjT99d4ZuxekvM4bhBxBodrjVWDAy4qJgwFa4LC1Qw-ja_JdVI-xDXYrtIYbaQ1BOEyeNtrelx7bSDv__3ND_Ay7ay_izmEreV8hR_hhb9f3i7mR2SfM3PU2edfG8fdJg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ba9VAEB5qFdQHq23V2KoRfBFMT7LX7GMRS4vHQ7FH6VvY7M5CQRM5l_r3nU1yQh-00LdAdjfLXNhvdjLzAbxHLBidIixzppaZcIXMjEOV1UIFg8xrq7pC4amezcrLS3O-BR_HWhhE7H4-w6P42OXyfevW8apsQmsKipHvwX0pBMv7aq0xZxApZ7vcptSZJuC_SUrmZjI_O6dQkBVHPLY_KSJ5DueCkEDkCL9xHnUEK__Hmt2Zc7Jzt90-hScDtkyPe2N4BlvY7MLOgDPTwYuXu_D4RhPCPSj6bqNpzPRMLpr2T_oNf7VkgGksPUkv1otrjNRENDf9ceWxXe7D95PP80-n2UCkkDku9CpzQXPPgmDaGOGUzz0PteVBOEt4zdXKKm-sUpKwg5WudMFbkqZHpTQrHefPYbtpG3wJac1D5LByda69YKhKqYMtZBlsoOBRsgQmG3FWbugyHskuflZdtJGbinRRRV1Ugy4S-DDO-N132Lhl7F6U8zhuEHEChxuNVYMLLismSgrXBAWsCbwbX5PzxIyIbbBdxzG6lKYkEJfAi17T49obA3n172--hYen86_Tano2-3IAj-Iu-5uZQ9heLdb4Gh6469XVcvGms9K_kOTfhQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Online+Rain%2FSnow+Removal+From+Surveillance+Videos&rft.jtitle=IEEE+transactions+on+image+processing&rft.au=Li%2C+Minghan&rft.au=Cao%2C+Xiangyong&rft.au=Zhao%2C+Qian&rft.au=Zhang%2C+Lei&rft.date=2021-01-01&rft.issn=1941-0042&rft.eissn=1941-0042&rft.volume=30&rft.spage=2029&rft_id=info:doi/10.1109%2FTIP.2021.3050313&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1057-7149&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1057-7149&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1057-7149&client=summon |