{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T10:38:33Z","timestamp":1751279913193,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030670696"},{"type":"electronic","value":"9783030670702"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-67070-2_27","type":"book-chapter","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T20:02:51Z","timestamp":1611950571000},"page":"453-467","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Self-calibrated Attention Neural Network for Real-World Super Resolution"],"prefix":"10.1007","author":[{"given":"Kaihua","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenhuan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126\u2013135 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"27_CR2","unstructured":"Anwar, S., Barnes, N.: Densely residual laplacian super-resolution. arXiv preprint arXiv:1906.12021 (2019)"},{"key":"27_CR3","unstructured":"Cai, J., Gu, S., Timofte, R., Zhang, L.: Ntire 2019 challenge on real image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)"},{"issue":"5","key":"27_CR4","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/TIP.2009.2012908","volume":"18","author":"S Dai","year":"2009","unstructured":"Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y., Katsaggelos, A.K.: Softcuts: a soft edge smoothness prior for color image super-resolution. IEEE Trans. Image Process. 18(5), 969\u2013981 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision \u2013 ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184\u2013199. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_13"},{"issue":"8","key":"27_CR6","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1175\/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2","volume":"18","author":"CE Duchon","year":"1979","unstructured":"Duchon, C.E.: Lanczos filtering in one and two dimensions. J. Appl. Meteorol. 18(8), 1016\u20131022 (1979)","journal-title":"J. Appl. Meteorol."},{"issue":"2","key":"27_CR7","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56\u201365 (2002)","journal-title":"IEEE Comput. Graphics Appl."},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-642-46466-9_18","volume-title":"Competition and Cooperation in Neural Nets","author":"K Fukushima","year":"1982","unstructured":"Fukushima, K., Miyake, S.: Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Amari, S., Arbib, M.A. (eds.) Competition and Cooperation in Neural Nets, pp. 267\u2013285. Springer, Berlin (1982)"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 349\u2013356. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Guo, Y., et al.: Closed-loop matters: Dual regression networks for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5407\u20135416 (2020)","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.: Real-world super-resolution via kernel estimation and noise injection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 466\u2013467 (2020)","DOI":"10.1109\/CVPRW50498.2020.00241"},{"issue":"6","key":"27_CR12","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoustics, Speech, and Signal Proces. 29(6), 1153\u20131160 (1981)","journal-title":"IEEE Trans. Acoustics, Speech, and Signal Proces."},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: 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":"27_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"27_CR15","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"27_CR16","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":"27_CR17","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Wang, C., Feng, J.: Improving convolutional networks with self-calibrated convolutions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10096\u201310105 (2020)","DOI":"10.1109\/CVPR42600.2020.01011"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., et al.: Aim 2019 challenge on real-world image super-resolution: methods and results. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3575\u20133583. IEEE (2019)","DOI":"10.1109\/ICCVW.2019.00442"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.: Structure-preserving super resolution with gradient guidance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7769\u20137778 (2020)","DOI":"10.1109\/CVPR42600.2020.00779"},{"issue":"3","key":"27_CR21","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10915-008-9214-8","volume":"37","author":"A Marquina","year":"2008","unstructured":"Marquina, A., Osher, S.J.: Image super-resolution by tv-regularization and bregman iteration. J. Sci. Comput. 37(3), 367\u2013382 (2008)","journal-title":"J. Sci. Comput."},{"issue":"1","key":"27_CR22","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TCI.2016.2629284","volume":"3","author":"Y Romano","year":"2016","unstructured":"Romano, Y., Isidoro, J., Milanfar, P.: Raisr: rapid and accurate image super resolution. IEEE Trans. Comput. Imaging 3(1), 110\u2013125 (2016)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3791\u20133799 (2015)","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Shang, T., Dai, Q., Zhu, S., Yang, T., Guo, Y.: Perceptual extreme super-resolution network with receptive field block. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 440\u2013441 (2020)","DOI":"10.1109\/CVPRW50498.2020.00228"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587659"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1920\u20131927 (2013)","DOI":"10.1109\/ICCV.2013.241"},{"key":"27_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-319-16817-3_8","volume-title":"Computer Vision \u2013 ACCV 2014","author":"R Timofte","year":"2015","unstructured":"Timofte, R., De\u00a0Smet, V., Van\u00a0Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111\u2013126. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16817-3_8"},{"key":"27_CR30","unstructured":"Timofte, R., Gu, S., Wu, J., Van Gool, L.: Ntire 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 852\u2013863 (2018)"},{"key":"27_CR31","unstructured":"Wei, P., Lu, H., Timofte, R., Lin, L., Zuo, W., et al.: AIM 2020 challenge on real image super-resolution. In: European Conference on Computer Vision Workshops (2020)"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Wei, P., et al.: Component divide-and-conquer for real-world image super-resolution. arXiv preprint arXiv:2008.01928 (2020)","DOI":"10.1007\/978-3-030-58598-3_7"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: Multi-grained attention networks for single image super-resolution. IEEE Trans. Circuits Syst. Video Technol. (2020)","DOI":"10.1109\/TCSVT.2020.2988895"},{"issue":"10","key":"27_CR34","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1109\/TIP.2015.2414877","volume":"24","author":"Q Yan","year":"2015","unstructured":"Yan, Q., Xu, Y., Yang, X., Nguyen, T.Q.: Single image superresolution based on gradient profile sharpness. IEEE Trans. Image Process. 24(10), 3187\u20133202 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"27_CR35","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861\u20132873 (2010)","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Yoo, J., Ahn, N., Sohn, K.A.: Rethinking data augmentation for image super-resolution: a comprehensive analysis and a new strategy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8375\u20138384 (2020)","DOI":"10.1109\/CVPR42600.2020.00840"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, K., Gool, L.V., Timofte, R.: Deep unfolding network for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3217\u20133226 (2020)","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"27_CR38","unstructured":"Zhang, K., Gu, S., Timofte, R.: Ntire 2020 challenge on perceptual extreme super-resolution: methods and results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 492\u2013493 (2020)"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"27_CR40","unstructured":"Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67070-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T23:04:25Z","timestamp":1738105465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67070-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030670696","9783030670702"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67070-2_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}