{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T06:52:50Z","timestamp":1726037570983},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811399169"},{"type":"electronic","value":"9789811399176"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-981-13-9917-6_52","type":"book-chapter","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T05:05:04Z","timestamp":1563512704000},"page":"541-552","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Image Super-Resolution Method Based on Cross Classification Trees and Cascaded Network"],"prefix":"10.1007","author":[{"given":"Yuan","family":"Lv","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiqi","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuejing","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,20]]},"reference":[{"issue":"6","key":"52_CR1","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TPAMI.2010.25","volume":"32","author":"KI Kim","year":"2010","unstructured":"Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127\u20131133 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"52_CR2","first-page":"508","volume":"26","author":"HS Hou","year":"1978","unstructured":"Hou, H.S., Andrews, H.C.: Cubic splines for image interpolation and digital ltering. IEEE Trans. Image Process. 26, 508\u2013517 (1978)","journal-title":"IEEE Trans. Image Process."},{"key":"52_CR3","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1109\/TIP.2008.924279","volume":"17","author":"X Zhang","year":"2008","unstructured":"Zhang, X., Wu, X.: Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation. IEEE Trans. Image Process. 17, 887\u2013896 (2008)","journal-title":"IEEE Trans. Image Process."},{"issue":"51","key":"52_CR4","first-page":"7003","volume":"4","author":"X Zhang","year":"2012","unstructured":"Zhang, X., Jiang, J., Li, J., Peng, S.: Manifold learning-based sample selection method for facial image super-resolution. Opt. Eng. 4(51), 7003 (2012)","journal-title":"Opt. Eng."},{"issue":"4","key":"52_CR5","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TIP.2011.2174371","volume":"21","author":"X Zhang","year":"2012","unstructured":"Zhang, X., Jiang, J., Peng, S.: Commutability of blur and affine warping in super-resolution with application to joint estimation of triple-coupled variables. IEEE Trans. Image Process. 21(4), 1796\u20131808 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"52_CR6","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1137\/040616024","volume":"4","author":"A Buades","year":"2005","unstructured":"Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. SIAM Journal on Multiscale Modeling and Simulation 4(2), 490\u2013530 (2005)","journal-title":"SIAM Journal on Multiscale Modeling and Simulation"},{"issue":"2","key":"52_CR7","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/TIP.2006.888330","volume":"16","author":"H Takeda","year":"2007","unstructured":"Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Processing 16(2), 349\u2013366 (2007)","journal-title":"IEEE Trans. Image Processing"},{"issue":"11","key":"52_CR8","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":"52_CR9","unstructured":"Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA, USA (2004)"},{"key":"52_CR10","unstructured":"Li, B., Chang, H., Shan, S., Chen, X.: Locality reserving constraints for super-resolution with neighbor embedding. In: 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt (2009)"},{"key":"52_CR11","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., Gool, L.V.: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision (2014)","DOI":"10.1109\/ICCV.2013.241"},{"key":"52_CR12","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V.D., Gool, L.V.: Anchored neighborhood regression for fast examplebased super-resolution. In: 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 1\u20138 December 2013","DOI":"10.1109\/ICCV.2013.241"},{"issue":"1","key":"52_CR13","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TIP.2015.2507402","volume":"25","author":"JS Choi","year":"2016","unstructured":"Choi, J.S., Kim, M.: Super-interpolation with edge-orientation-based mapping kernels for low complex 2x upscaling. IEEE Trans. Image Process. 25(1), 469\u2013482 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"52_CR14","unstructured":"Zhao, Y., Wang, R., Jia, W., Yang, J., Wang, W., Gao, W.: Local patch classification based framework for single image super-resolution, arXiv:1703.04088 (2017)"},{"key":"52_CR15","doi-asserted-by":"crossref","unstructured":"Romano, Y., Isidoro, J., Milanfar, P.: Rapid and accurate image super resolution, arXiv:1606.01299 , June 2016","DOI":"10.1109\/TCI.2016.2629284"},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network, arXiv:1608.00367 , August 2016","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"52_CR17","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"},{"key":"52_CR18","unstructured":"Tanno, R., Arulkumaran, K., Alexander, D.C., Criminisi, A., Nori, A.: Adaptive neural trees, arXiv:1807.06699v1 (2018)"},{"issue":"5","key":"52_CR19","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1109\/TCSVT.2015.2513661","volume":"27","author":"J Huang","year":"2017","unstructured":"Huang, J.: Learning hierarchical decision trees for single-image super-resolution. IEEE Trans. Circuits Syst. Video Technol. 27(5), 937\u2013950 (2017)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"10","key":"52_CR20","doi-asserted-by":"publisher","first-page":"3232","DOI":"10.1109\/TIP.2015.2440751","volume":"24","author":"J Huang","year":"2015","unstructured":"Huang, J., Siu, W., Liu, T.: Fast image interpolation via random forests. IEEE Trans. Image Process. 24(10), 3232\u20133245 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"52_CR21","unstructured":"Liu, Z., Siu, W.: Cascaded ramdom forests for fast image super-resolution. In: IEEE International Conference on Image Processing (2018)"},{"key":"52_CR22","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (2017)","DOI":"10.24963\/ijcai.2017\/497"},{"key":"52_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-319-54407-6_13","volume-title":"Computer Vision \u2013 ACCV 2016 Workshops","author":"R Wang","year":"2017","unstructured":"Wang, R., Han, C., Li, M., Guo, T.: Single image super-resolution reconstruction based on edge-preserving with external and internal gradient prior knowledge. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10116, pp. 191\u2013205. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54407-6_13"}],"container-title":["Communications in Computer and Information Science","Image and Graphics Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-13-9917-6_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T02:59:35Z","timestamp":1663988375000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-13-9917-6_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9789811399169","9789811399176"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-13-9917-6_52","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"20 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IGTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Image and Graphics Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"igta0","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.bsig.org.cn\/list\/IGTA","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"152","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":"66","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":"43% - 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":"8","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}