{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:45:48Z","timestamp":1783597548602,"version":"3.55.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031500688","type":"print"},{"value":"9783031500695","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-50069-5_30","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:02:34Z","timestamp":1705644154000},"page":"362-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Efficient and\u00a0Lightweight Structure for\u00a0Spatial-Temporal Feature Extraction in\u00a0Video Super Resolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7263-7940","authenticated-orcid":false,"given":"Xiaonan","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4320-8825","authenticated-orcid":false,"given":"Yukun","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1543-1589","authenticated-orcid":false,"given":"Yuansong","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4608-1451","authenticated-orcid":false,"given":"Yuhang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"issue":"3","key":"30_CR1","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TPAMI.2019.2941941","volume":"43","author":"W Bao","year":"2019","unstructured":"Bao, W., Lai, W.S., Zhang, X., Gao, Z., Yang, M.H.: MEMC-Net: motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 933\u2013948 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4778\u20134787 (2017)","DOI":"10.1109\/CVPR.2017.304"},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Cao, J., et al.: Towards interpretable video super-resolution via alternating optimization. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol. 13678, pp. 393\u2013411. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19797-0_23","DOI":"10.1007\/978-3-031-19797-0_23"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: Understanding deformable alignment in video super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 973\u2013981 (2021)","DOI":"10.1609\/aaai.v35i2.16181"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Chiche, B.N., Woiselle, A., Frontera-Pons, J., Starck, J.L.: Stable long-term recurrent video super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 837\u2013846 (2022)","DOI":"10.1109\/CVPR52688.2022.00091"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"30_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391\u2013407. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_25"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., Oh, J., Kim, M.: Deep SR-ITM: joint learning of super-resolution and inverse tone-mapping for 4K UHD HDR applications. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3116\u20133125 (2019)","DOI":"10.1109\/ICCV.2019.00321"},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"105267","DOI":"10.1016\/j.compbiomed.2022.105267","volume":"143","author":"I Kiran","year":"2022","unstructured":"Kiran, I., Raza, B., Ijaz, A., Khan, M.A.: DenseRes-Unet: segmentation of overlapped\/clustered nuclei from multi organ histopathology images. Comput. Biol. Med. 143, 105267 (2022)","journal-title":"Comput. Biol. Med."},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"30_CR12","unstructured":"Liu, H., et al.: A single frame and multi-frame joint network for 360-degree panorama video super-resolution. arXiv preprint arXiv:2008.10320 (2020)"},{"issue":"8","key":"30_CR13","doi-asserted-by":"publisher","first-page":"5981","DOI":"10.1007\/s10462-022-10147-y","volume":"55","author":"H Liu","year":"2022","unstructured":"Liu, H., et al.: Video super-resolution based on deep learning: a comprehensive survey. Artif. Intell. Rev. 55(8), 5981\u20136035 (2022)","journal-title":"Artif. Intell. Rev."},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Nah, S., et al.: Ntire 2019 challenge on video deblurring and super-resolution: dataset and study. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0\u20130 (2019)","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TIP.2021.3136619","volume":"31","author":"A Nazir","year":"2021","unstructured":"Nazir, A., et al.: ECSU-Net: an embedded clustering sliced u-net coupled with fusing strategy for efficient intervertebral disc segmentation and classification. IEEE Trans. Image Process. 31, 880\u2013893 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6626\u20136634 (2018)","DOI":"10.1109\/CVPR.2018.00693"},{"issue":"2","key":"30_CR17","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TIP.2009.2034992","volume":"19","author":"K Seshadrinathan","year":"2009","unstructured":"Seshadrinathan, K., Bovik, A.C.: Motion tuned spatio-temporal quality assessment of natural videos. IEEE Trans. Image Process. 19(2), 335\u2013350 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0\u20130 (2019)","DOI":"10.1109\/CVPRW.2019.00247"},{"issue":"4","key":"30_CR19","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"30_CR20","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1007\/s00371-022-02414-4","volume":"39","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Ran, Z., Mabu, S., Li, Y., Li, L.: Saunet++: an automatic segmentation model of COVID-19 lesion from CT slices. Vis. Comput. 39(6), 2291\u20132304 (2023)","journal-title":"Vis. Comput."},{"key":"30_CR21","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1007\/s11263-018-01144-2","volume":"127","author":"T Xue","year":"2019","unstructured":"Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vision 127, 1106\u20131125 (2019)","journal-title":"Int. J. Comput. Vision"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5791\u20135800 (2020)","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"30_CR23","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1109\/LSP.2020.3013518","volume":"27","author":"X Ying","year":"2020","unstructured":"Ying, X., Wang, L., Wang, Y., Sheng, W., An, W., Guo, Y.: Deformable 3D convolution for video super-resolution. IEEE Signal Process. Lett. 27, 1500\u20131504 (2020)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50069-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:07:15Z","timestamp":1705644435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50069-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031500688","9783031500695"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50069-5_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Xiaonan He and Yukun Xia contributed equally to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Contribution Statement"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"385","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"149","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}