Loading…
Deep convolution network for surveillance records super-resolution
The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a dee...
Saved in:
| Published in: | Multimedia tools and applications 2019-09, Vol.78 (17), p.23815-23829 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553 |
| container_end_page | 23829 |
| container_issue | 17 |
| container_start_page | 23815 |
| container_title | Multimedia tools and applications |
| container_volume | 78 |
| creator | Shamsolmoali, Pourya Zareapoor, Masoumeh Jain, Deepak Kumar Jain, Vinay Kumar Yang, Jie |
| description | The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at
https://github.com/Mzareapoor/Super-resolution |
| doi_str_mv | 10.1007/s11042-018-5915-7 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2021755600</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2021755600</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553</originalsourceid><addsrcrecordid>eNp1kMFKxDAQhoMouK4-gLeC5-hM2jTtUVdXhQUveg7ZZCq71qYm7Ypvb5YuePI0w_B__8DH2CXCNQKom4gIheCAFZc1Sq6O2AylyrlSAo_TnlfAlQQ8ZWcxbgGwlKKYsbt7oj6zvtv5dhw2vss6Gr59-MgaH7I4hh1t2tZ0lrJA1gcX07GnwAPFA3HOThrTRro4zDl7Wz68Lp746uXxeXG74jbHcuAOCQpZAlojrXO4diXUYCw5J5yUEoyrTWllRWtTNKpoSksVibyu13WBUuZzdjX19sF_jRQHvfVj6NJLLUCgkqkcUgqnlA0-xkCN7sPm04QfjaD3qvSkSidVeq9Kq8SIiYkp271T-Gv-H_oFaOhs6w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2021755600</pqid></control><display><type>article</type><title>Deep convolution network for surveillance records super-resolution</title><source>ABI/INFORM Global</source><source>Springer Nature</source><creator>Shamsolmoali, Pourya ; Zareapoor, Masoumeh ; Jain, Deepak Kumar ; Jain, Vinay Kumar ; Yang, Jie</creator><creatorcontrib>Shamsolmoali, Pourya ; Zareapoor, Masoumeh ; Jain, Deepak Kumar ; Jain, Vinay Kumar ; Yang, Jie</creatorcontrib><description>The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at
https://github.com/Mzareapoor/Super-resolution</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-018-5915-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Computer Communication Networks ; Computer Science ; Convolution ; Data Structures and Information Theory ; Image resolution ; Machine learning ; Model testing ; Multimedia Information Systems ; Neural networks ; Special Purpose and Application-Based Systems ; Surveillance</subject><ispartof>Multimedia tools and applications, 2019-09, Vol.78 (17), p.23815-23829</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Multimedia Tools and Applications is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553</citedby><cites>FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553</cites><orcidid>0000-0002-0263-1661</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2021755600/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2021755600?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11687,27923,27924,36059,44362,74666</link.rule.ids></links><search><creatorcontrib>Shamsolmoali, Pourya</creatorcontrib><creatorcontrib>Zareapoor, Masoumeh</creatorcontrib><creatorcontrib>Jain, Deepak Kumar</creatorcontrib><creatorcontrib>Jain, Vinay Kumar</creatorcontrib><creatorcontrib>Yang, Jie</creatorcontrib><title>Deep convolution network for surveillance records super-resolution</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at
https://github.com/Mzareapoor/Super-resolution</description><subject>Artificial neural networks</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Convolution</subject><subject>Data Structures and Information Theory</subject><subject>Image resolution</subject><subject>Machine learning</subject><subject>Model testing</subject><subject>Multimedia Information Systems</subject><subject>Neural networks</subject><subject>Special Purpose and Application-Based Systems</subject><subject>Surveillance</subject><issn>1380-7501</issn><issn>1573-7721</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kMFKxDAQhoMouK4-gLeC5-hM2jTtUVdXhQUveg7ZZCq71qYm7Ypvb5YuePI0w_B__8DH2CXCNQKom4gIheCAFZc1Sq6O2AylyrlSAo_TnlfAlQQ8ZWcxbgGwlKKYsbt7oj6zvtv5dhw2vss6Gr59-MgaH7I4hh1t2tZ0lrJA1gcX07GnwAPFA3HOThrTRro4zDl7Wz68Lp746uXxeXG74jbHcuAOCQpZAlojrXO4diXUYCw5J5yUEoyrTWllRWtTNKpoSksVibyu13WBUuZzdjX19sF_jRQHvfVj6NJLLUCgkqkcUgqnlA0-xkCN7sPm04QfjaD3qvSkSidVeq9Kq8SIiYkp271T-Gv-H_oFaOhs6w</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Shamsolmoali, Pourya</creator><creator>Zareapoor, Masoumeh</creator><creator>Jain, Deepak Kumar</creator><creator>Jain, Vinay Kumar</creator><creator>Yang, Jie</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-0263-1661</orcidid></search><sort><creationdate>20190901</creationdate><title>Deep convolution network for surveillance records super-resolution</title><author>Shamsolmoali, Pourya ; Zareapoor, Masoumeh ; Jain, Deepak Kumar ; Jain, Vinay Kumar ; Yang, Jie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Convolution</topic><topic>Data Structures and Information Theory</topic><topic>Image resolution</topic><topic>Machine learning</topic><topic>Model testing</topic><topic>Multimedia Information Systems</topic><topic>Neural networks</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Surveillance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shamsolmoali, Pourya</creatorcontrib><creatorcontrib>Zareapoor, Masoumeh</creatorcontrib><creatorcontrib>Jain, Deepak Kumar</creatorcontrib><creatorcontrib>Jain, Vinay Kumar</creatorcontrib><creatorcontrib>Yang, Jie</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</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>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</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>ProQuest Central Basic</collection><jtitle>Multimedia tools and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shamsolmoali, Pourya</au><au>Zareapoor, Masoumeh</au><au>Jain, Deepak Kumar</au><au>Jain, Vinay Kumar</au><au>Yang, Jie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep convolution network for surveillance records super-resolution</atitle><jtitle>Multimedia tools and applications</jtitle><stitle>Multimed Tools Appl</stitle><date>2019-09-01</date><risdate>2019</risdate><volume>78</volume><issue>17</issue><spage>23815</spage><epage>23829</epage><pages>23815-23829</pages><issn>1380-7501</issn><eissn>1573-7721</eissn><abstract>The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at
https://github.com/Mzareapoor/Super-resolution</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-018-5915-7</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-0263-1661</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1380-7501 |
| ispartof | Multimedia tools and applications, 2019-09, Vol.78 (17), p.23815-23829 |
| issn | 1380-7501 1573-7721 |
| language | eng |
| recordid | cdi_proquest_journals_2021755600 |
| source | ABI/INFORM Global; Springer Nature |
| subjects | Artificial neural networks Computer Communication Networks Computer Science Convolution Data Structures and Information Theory Image resolution Machine learning Model testing Multimedia Information Systems Neural networks Special Purpose and Application-Based Systems Surveillance |
| title | Deep convolution network for surveillance records super-resolution |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T08%3A25%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20convolution%20network%20for%20surveillance%20records%20super-resolution&rft.jtitle=Multimedia%20tools%20and%20applications&rft.au=Shamsolmoali,%20Pourya&rft.date=2019-09-01&rft.volume=78&rft.issue=17&rft.spage=23815&rft.epage=23829&rft.pages=23815-23829&rft.issn=1380-7501&rft.eissn=1573-7721&rft_id=info:doi/10.1007/s11042-018-5915-7&rft_dat=%3Cproquest_cross%3E2021755600%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c316t-d1e045601ca5cdd1bd6090acedd2d5550ad9a6c58eba4f74f6ce8e2399b941553%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2021755600&rft_id=info:pmid/&rfr_iscdi=true |