Efficient and High-Quality Monocular Depth Estimation via Gated Multi-Scale Network
The key issue in monocular depth estimation is how to construct the depth image better and improve the quality of the depth map. At present, most of the monocular depth estimation methods based on deep learning manipulate images at low resolution that leads to loss of detail and blurring of boundari...
Saved in:
| Published in | IEEE access Vol. 8; pp. 7709 - 7718 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The key issue in monocular depth estimation is how to construct the depth image better and improve the quality of the depth map. At present, most of the monocular depth estimation methods based on deep learning manipulate images at low resolution that leads to loss of detail and blurring of boundaries. Nevertheless, deep learning with a large number of parameters needs highly computational complexity, which makes it difficult to apply high-resolution (HR) images to the depth estimate. In this work, model accuracy and runtime are two important factors to be considered. To improve the depth map quality and reduce the running time of the network, we introduce super-resolution techniques as methods of up-sampling to generate high-quality depth images at a faster rate for the depth estimation network. A novel approach is proposed for collecting high-level features that are captured under different receptive fields. The gated multi-scale decoder allows us to effectively filter information by the gated module. By combining the gated module to aid the super resolution of depth images, our method reduces memory consumption while improves reconstruction quality. Experiment results on the challenging NYU Depth v2 dataset demonstrate that both contributions provide significant performance gains over the state-of-the-art in self-supervised depth estimation. |
|---|---|
| AbstractList | The key issue in monocular depth estimation is how to construct the depth image better and improve the quality of the depth map. At present, most of the monocular depth estimation methods based on deep learning manipulate images at low resolution that leads to loss of detail and blurring of boundaries. Nevertheless, deep learning with a large number of parameters needs highly computational complexity, which makes it difficult to apply high-resolution (HR) images to the depth estimate. In this work, model accuracy and runtime are two important factors to be considered. To improve the depth map quality and reduce the running time of the network, we introduce super-resolution techniques as methods of up-sampling to generate high-quality depth images at a faster rate for the depth estimation network. A novel approach is proposed for collecting high-level features that are captured under different receptive fields. The gated multi-scale decoder allows us to effectively filter information by the gated module. By combining the gated module to aid the super resolution of depth images, our method reduces memory consumption while improves reconstruction quality. Experiment results on the challenging NYU Depth v2 dataset demonstrate that both contributions provide significant performance gains over the state-of-the-art in self-supervised depth estimation. |
| Author | Lin, Lixiong Zhang, Liwei He, Bingwei Huang, Guohui Chen, Yanjie |
| Author_xml | – sequence: 1 givenname: Lixiong orcidid: 0000-0002-9829-5358 surname: Lin fullname: Lin, Lixiong email: linlixiong@126.com organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China – sequence: 2 givenname: Guohui surname: Huang fullname: Huang, Guohui organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China – sequence: 3 givenname: Yanjie surname: Chen fullname: Chen, Yanjie organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China – sequence: 4 givenname: Liwei orcidid: 0000-0003-3083-9002 surname: Zhang fullname: Zhang, Liwei organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China – sequence: 5 givenname: Bingwei orcidid: 0000-0002-4386-8542 surname: He fullname: He, Bingwei organization: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China |
| BookMark | eNqFUUtv1DAQtlCRKKW_oBdLnLP4HedYLUtbqQWhhbM1a49bLyFeHKeo_560qSrEhbnMaDTfQ_O9JUdDHpCQM85WnLPuw_l6vdluV4IJthKdUa2Ur8ix4KZrpJbm6K_5DTkdxz2by84r3R6T7SbG5BMOlcIQ6GW6vWu-TtCn-kBv8pD91EOhH_FQ7-hmrOkn1JQHep-AXkDFQG-mvqZm66FH-hnr71x-vCOvI_Qjnj73E_L90-bb-rK5_nJxtT6_brxitjYKg4jAY8SAkaOP3oCyJtjQcb8zNoIN1rCovcBWsSAZVyCVD1ZwL8xOnpCrhTdk2LtDmc2VB5chuadFLrcOSk2-R6c5Uzx6JWJQKli0WgPzdsdbYdjOm5nr_cJ1KPnXhGN1-zyVYbbvhNKqtVwbOV_J5cqXPI4F44sqZ-4xDLeE4R7DcM9hzKjuH5RP9emPtUDq_4M9W7AJEV_UbKc5b1v5B9wcmX0 |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_3390_s21010054 crossref_primary_10_1007_s00521_022_07663_x crossref_primary_10_1145_3386569_3392420 crossref_primary_10_1109_ACCESS_2021_3076346 crossref_primary_10_1109_ACCESS_2020_3016008 |
| Cites_doi | 10.1109/CVPR.2018.00214 10.1109/ICCV.2015.336 10.1007/978-3-030-01237-3_32 10.1109/CVPR.2017.699 10.1109/CVPRW.2017.151 10.1109/CVPR.2018.00412 10.1109/CVPR.2018.00179 10.1016/j.media.2018.10.004 10.1109/CVPR.2009.5206848 10.1109/CVPR.2016.438 10.1109/CVPR.2017.106 10.1109/CVPR.2016.207 10.1109/ICCV.2017.365 10.1109/3DV.2016.32 10.1109/CVPR.2018.00212 10.1109/CVPR.2017.694 10.1109/CVPR.2018.00042 10.1109/CVPR.2018.00037 10.1109/CVPR.2016.182 10.1109/ICCV.2015.316 10.1109/CVPR.2016.90 10.1109/ICCV.2017.514 10.1109/ICRA.2018.8460184 10.1109/CVPR.2017.700 10.1109/ICCV.2015.169 10.1109/WACV.2016.7477595 10.1109/CVPR.2015.7298965 10.1109/ICCV.2015.304 10.1109/WACV.2019.00116 10.1109/ICRA.2019.8794182 10.1109/TPAMI.2019.2913372 10.1007/978-3-642-33715-4_54 10.1109/CVPR.2018.00474 10.1109/CVPR.2015.7299152 10.1109/CVPR.2018.00043 10.1109/CVPR.2017.196 10.1109/ICIP.2018.8451408 10.1109/CVPR.2018.00216 10.1109/CVPR.2017.243 10.1109/3DV.2018.00043 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2020.2964733 |
| DatabaseName | IEEE Xplore (IEEE) Open Access资源_IEL Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts 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 METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEL url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 7718 |
| ExternalDocumentID | oai_doaj_org_article_51041fc42fd44d8e855a0c8b17260bc6 10_1109_ACCESS_2020_2964733 8951177 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Natural Science Foundation of Fujian Province grantid: 2019J05024 funderid: 10.13039/501100003392 – fundername: Department of Education, Fujian Province grantid: JAT170091 funderid: 10.13039/501100003410 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION RIG 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c408t-4ed2fa1ffedef1ecfc6a486d8d91cb68fa8d860f5c2e740d3014a34cd821c26b3 |
| IEDL.DBID | DOA |
| ISSN | 2169-3536 |
| IngestDate | Wed Aug 27 01:23:26 EDT 2025 Mon Jun 30 03:50:24 EDT 2025 Thu Apr 24 22:56:46 EDT 2025 Tue Jul 01 01:22:03 EDT 2025 Wed Aug 27 02:41:49 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c408t-4ed2fa1ffedef1ecfc6a486d8d91cb68fa8d860f5c2e740d3014a34cd821c26b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-4386-8542 0000-0003-3083-9002 0000-0002-9829-5358 |
| OpenAccessLink | https://doaj.org/article/51041fc42fd44d8e855a0c8b17260bc6 |
| PQID | 2454781563 |
| PQPubID | 4845423 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1109_ACCESS_2020_2964733 ieee_primary_8951177 crossref_citationtrail_10_1109_ACCESS_2020_2964733 doaj_primary_oai_doaj_org_article_51041fc42fd44d8e855a0c8b17260bc6 proquest_journals_2454781563 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20200000 2020-00-00 20200101 2020-01-01 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – year: 2020 text: 20200000 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2020 |
| 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 | ref13 ref12 ref15 ref14 chen (ref35) 2017 ref52 ref11 ref10 kingma (ref49) 2015 ref17 ref16 ref19 ref18 ref51 ref50 ref45 ref48 ref47 ref42 ref41 ref8 dong (ref36) 2014 ref7 ref9 eigen (ref28) 2014 ref4 ref3 ref6 ref5 ref40 ref34 ref37 ref31 ref30 ref32 alhashim (ref44) 2018 stollenga (ref2) 2014 ref39 li (ref33) 2015 ref38 chakrabarti (ref29) 2016 ref24 ref23 ref25 ref20 ref22 paszke (ref46) 2017 ref21 ronneberger (ref43) 2015 singh (ref26) 2018 ref27 godard (ref1) 2018 |
| References_xml | – ident: ref5 doi: 10.1109/CVPR.2018.00214 – ident: ref12 doi: 10.1109/ICCV.2015.336 – year: 2017 ident: ref46 article-title: Automatic differentiation in PyTorch publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref24 doi: 10.1007/978-3-030-01237-3_32 – ident: ref14 doi: 10.1109/CVPR.2017.699 – start-page: 184 year: 2014 ident: ref36 article-title: Learning a deep convolutional network for image super-resolution publication-title: Proc Eur Conf Comput Vis (ECCV) – year: 2017 ident: ref35 article-title: Rethinking atrous convolution for semantic image segmentation publication-title: arXiv 1706 05587 – ident: ref23 doi: 10.1109/CVPRW.2017.151 – ident: ref42 doi: 10.1109/CVPR.2018.00412 – ident: ref40 doi: 10.1109/CVPR.2018.00179 – ident: ref21 doi: 10.1016/j.media.2018.10.004 – ident: ref48 doi: 10.1109/CVPR.2009.5206848 – start-page: 2658 year: 2016 ident: ref29 article-title: Depth from a single image by harmonizing overcomplete local network predictions publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref11 doi: 10.1109/CVPR.2016.438 – start-page: 1 year: 2015 ident: ref49 article-title: Adam: A method for stochastic optimization publication-title: Proc Int Conf Learn Represent (ICLR) – start-page: 2366 year: 2014 ident: ref28 article-title: Depth map prediction from a single image using a multi-scale deep network publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref25 doi: 10.1109/CVPR.2017.106 – year: 2018 ident: ref44 article-title: High quality monocular depth estimation via transfer learning publication-title: arXiv 1812 11941 – ident: ref38 doi: 10.1109/CVPR.2016.207 – ident: ref30 doi: 10.1109/ICCV.2017.365 – ident: ref4 doi: 10.1109/3DV.2016.32 – ident: ref16 doi: 10.1109/CVPR.2018.00212 – ident: ref13 doi: 10.1109/CVPR.2017.694 – ident: ref18 doi: 10.1109/CVPR.2018.00042 – ident: ref32 doi: 10.1109/CVPR.2018.00037 – ident: ref37 doi: 10.1109/CVPR.2016.182 – ident: ref10 doi: 10.1109/ICCV.2015.316 – ident: ref41 doi: 10.1109/CVPR.2016.90 – ident: ref39 doi: 10.1109/ICCV.2017.514 – ident: ref7 doi: 10.1109/ICRA.2018.8460184 – ident: ref15 doi: 10.1109/CVPR.2017.700 – ident: ref27 doi: 10.1109/ICCV.2015.169 – ident: ref20 doi: 10.1109/WACV.2016.7477595 – ident: ref22 doi: 10.1109/CVPR.2015.7298965 – ident: ref19 doi: 10.1109/ICCV.2015.304 – ident: ref6 doi: 10.1109/WACV.2019.00116 – start-page: 234 year: 2015 ident: ref43 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput -Assist Intervent (MICCAI) – ident: ref45 doi: 10.1109/ICRA.2019.8794182 – ident: ref51 doi: 10.1109/TPAMI.2019.2913372 – ident: ref47 doi: 10.1007/978-3-642-33715-4_54 – year: 2018 ident: ref1 article-title: Digging into self-supervised monocular depth estimation publication-title: arXiv 1806 01260 – start-page: 9310 year: 2018 ident: ref26 article-title: SNIPER: Efficient multi-scale training publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref52 doi: 10.1109/CVPR.2018.00474 – ident: ref9 doi: 10.1109/CVPR.2015.7299152 – year: 2014 ident: ref2 article-title: Deep networks with internal selective attention through feedback connections publication-title: Proc Adv Neural Inf Process Syst (NIPS) – ident: ref8 doi: 10.1109/CVPR.2018.00043 – ident: ref3 doi: 10.1109/CVPR.2017.196 – ident: ref34 doi: 10.1109/ICIP.2018.8451408 – ident: ref17 doi: 10.1109/CVPR.2018.00216 – ident: ref50 doi: 10.1109/CVPR.2017.243 – start-page: 1119 year: 2015 ident: ref33 article-title: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs publication-title: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) – ident: ref31 doi: 10.1109/3DV.2018.00043 |
| SSID | ssj0000816957 |
| Score | 2.2225654 |
| Snippet | The key issue in monocular depth estimation is how to construct the depth image better and improve the quality of the depth map. At present, most of the... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 7709 |
| SubjectTerms | Blurring Convolution Decoding Deep learning Depth estimation Estimation Feature extraction gated multi-scale network Image manipulation Image quality Image reconstruction Image resolution Logic gates Model accuracy Modules monocular vision super resolution |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3JbhQxELWSnODAFhADCfKBYzzx1m73MUwmipCSS4iUm-WlLBBoJoIeJPL18TYtNiFurZbdqvbzUlWueoXQWydcUtOjI057SaQCIC4KINFaD2LQMqic73xxqc6v5fub7mYHHU25MABQgs9gnh_LXX5Y-012lR3rpA6wvt9Fu8lwq7lakz8lF5AYur4RCzE6HJ8sFukfkgnI6TxfLvZC_HL4FI7-VlTlj524HC9nj9HFVrAaVfJ5vhnd3N_9xtn4v5I_QY-anolP6sR4inZg9Qw9_Il9cB9dLQt9ROqI7SrgHPFBKqXGD5yW-rpEqOJTuB0_4mXaCWqSI_7-yeLscwu4JO-Sq4Qy4MsaTv4cXZ8tPyzOSauxQLykeiQSAo-WxQgBIgMfvbJSq6DDwLxTOlodtKKx8xx6SUO2wKyQPmjOPFdOvEB7q_UKXiLcO5f0DWl11FoyGQYqvE0aSmDcQeRqhvh28I1vBOS5DsYXUwwROpiKmMmImYbYDB1NnW4r_8a_m7_LqE5NM3l2eZHQMG0tmrQNSRa95DFIGTTorrPUa5d0OUWdT4LuZwSnjzTwZuhgO0dMW-jfDC-EaMkIFq_-3us1epAFrF6bA7Q3ft3AYdJjRvemTOB7Ed3wUA priority: 102 providerName: IEEE |
| Title | Efficient and High-Quality Monocular Depth Estimation via Gated Multi-Scale Network |
| URI | https://ieeexplore.ieee.org/document/8951177 https://www.proquest.com/docview/2454781563 https://doaj.org/article/51041fc42fd44d8e855a0c8b17260bc6 |
| Volume | 8 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3JSgQxEA3iSQ_iiuNGDh6NZut09VHHERH0ooK3kBUFGUVHwb83SzuMCHrx2qSXVFVSr9JVrxDat8ImmB4tseAkkSoEYqMIJBrjguhAepXrnS-v1PmtvLhr7mZafeWcsEoPXAV3lGxGsugkj15KDwGaxlAHNjleRa0rZNvJ580EU2UPBqa6pu1phhjtjo6HwzSjFBByeph_NbZCfHNFhbG_b7HyY18uzuZsGS31KBEf169bQXNhvIoWZ7gD19D1qJA_JJ-BzdjjnK9BKiHGB04L9ankl-LT8Dy5x6O0jmuJIn5_MDifmHlcSm_JddJRwFc1GXwd3Z6NbobnpO-QQJykMCEyeB4NizH4EFlw0SkjQXnwHXNWQTTgQdHYOB5aSX2On4yQzgNnjisrNtD8-GkcNhFurU1oQRqIAJJJ31HhTMIXnnEbIlcDxL-EpV1PH567WDzqEkbQTlcJ6yxh3Ut4gA6mNz1X9ozfh59kLUyHZurrciEZhO4NQv9lEAO0lnU4fQgkDMnadoB2vnSq-2X6qnmhM0shrNj6j1dvo4U8nXpCs4PmJy9vYTdhlondK-a5V8oLPwFvJedk |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3NbtQwELZKOQAHCpSKhQI-wI1sE8dxnEMPZbvVlrZ7aSv1ZvwzVivQbkWzoPIsvArvxtjxRvyJWyVuUWRbjv1lPDOe-YaQV6Y0qKZ7kxlpecYFQGZ8CZnX2kLZSO5EyHc-morJKX93Vp2tkG99LgwAxOAzGIbHeJfv5nYRXGVbEtWBoq5TCOUBXH9BA-1qe38Xd_M1Y3vjk9EkSzUEMstz2WYcHPO68B4c-AKst0JzKZx0TWGNkF5LJ0XuK8ug5rkLFoYuuXWSFZYJU-K4t8ht1DMq1mWH9R6cULKiqepEZVTkzdbOaISrhkYny4fhOrMuy1-Ou1gVIJVx-UP2xwNtb418Xy5FF8fyYbhozdB-_Y0l8n9dqwfkftKk6U4H_YdkBWaPyL2f-BXXyfE4EmTgRKmeORpiWrKONOSaojCbxxhcuguX7Tkdo6zr0jjp5wtNg1fR0ZienB0jjoFOu4D5x-T0Rr5qg6zO5jN4QmhtDGpUXEsvJS-4a_LSatTBXMEMeCYGhC03W9lEsR4qfXxU0dTKG9UhRAWEqISQAXnTd7rsGEb-3fxtQFHfNNCDxxe4-ypJG4WClhfecuYd506CrCqdW2lQWxW5sTjR9YCYfpAElgHZXGJSJVF2pVikfEMzv3z6914vyZ3JydGhOtyfHjwjd8NkOx_VJlltPy3gOWptrXkRfx5K3t80An8AJrZRdA |
| 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=Efficient+and+High-Quality+Monocular+Depth+Estimation+via+Gated+Multi-Scale+Network&rft.jtitle=IEEE+access&rft.au=Lin%2C+Lixiong&rft.au=Huang%2C+Guohui&rft.au=Chen%2C+Yanjie&rft.au=Zhang%2C+Liwei&rft.date=2020&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=8&rft.spage=7709&rft.epage=7718&rft_id=info:doi/10.1109%2FACCESS.2020.2964733&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2020_2964733 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |