{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:28:42Z","timestamp":1743017322608,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030624620"},{"type":"electronic","value":"9783030624637"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62463-7_31","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T09:06:23Z","timestamp":1604999183000},"page":"336-346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dictionary Learning Based on Structural Self-similarity and Convolution Neural Network"],"prefix":"10.1007","author":[{"given":"Ling","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Wenchao","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Xiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"24","key":"31_CR1","doi-asserted-by":"publisher","first-page":"2874","DOI":"10.1109\/TIP.2015.2432713","volume":"9","author":"ZHX Li","year":"2015","unstructured":"Li, Z.H.X., He, H., Wang, R., et al.: Single image super-resolution bidirectional group sparsity and directional features. Image Process. 9(24), 2874\u20132888 (2015)","journal-title":"Image Process."},{"key":"31_CR2","unstructured":"Dong, C., Loy, C.C., He, K., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 2(38), 295\u2013307 (2016)"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Yang, J., Wright, J., Huang, T., et al.: Image super-resolution via spare representation. IEEE Trans. Image Process. 19(11), 2861\u20132873 (2010)","DOI":"10.1109\/TIP.2010.2050625"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Timofte, R., Smet, V., Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision, Sydney, pp. 1920\u20131927. IEEE (2013)","DOI":"10.1109\/ICCV.2013.241"},{"key":"31_CR5","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\u2009+\u2009: 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":"31_CR6","doi-asserted-by":"crossref","unstructured":"Yang, C.Y., Yang, M.H.: Fast direct super-resolution by simple functions. In: IEEE Interaction Conference on Computer Vision, Sydney, pp. 561\u2013568. IEEE (2013)","DOI":"10.1109\/ICCV.2013.75"},{"key":"31_CR7","unstructured":"Dai, D., Timoft, R., Vangool, L.: Jointly optimized regressors for image super-resolution. Comput. Graph. Forum. 34(2), 95\u2013104 (2015)"},{"issue":"2","key":"31_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1145\/1944846.1944852","volume":"30","author":"G Freedman","year":"2011","unstructured":"Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30(2), 12 (2011)","journal-title":"ACM Trans. Graph."},{"issue":"1","key":"31_CR9","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TIP.2008.2008067","volume":"18","author":"M Protter","year":"2009","unstructured":"Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans. Image Process. 18(1), 36\u201351 (2009)","journal-title":"IEEE Trans. Image Process."},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: IEEE International Conference on Computer Vision. Kyoto, Japan, pp. 2272\u20132279. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459452"},{"key":"31_CR11","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":"31_CR12","doi-asserted-by":"crossref","unstructured":"Gkasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: International Conference on Computer Vision, Kyoto, pp. 349\u2013356. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Candocia, F.M., Principe, J.C.: Super-resolution of images based on local correlations. IEEE Interact. Neural Netw. 2(10), 372\u2013380 (1999)","DOI":"10.1109\/72.750566"},{"issue":"22","key":"31_CR14","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","volume":"4","author":"WS Dong","year":"2013","unstructured":"Dong, W.S., Zhang, L., Shi, G.M., et al.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 4(22), 1620\u20131630 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"31_CR15","unstructured":"You, X., Xue, W., et al.: Single image super-resolution with non-local balanced low-rank matrix restoration. In: International Conference on Pattern Recognition, Cancun, vol. 10, no. 23, pp. 1255\u20131260. IEEE (2016)"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, L., Zuo, W., et al.: Patch group based nonlocal self-similarity prior learning for image denoising. In: Proceedings of IEEE Conference on Computer Vision, Santiago, pp. 244\u2013252. IEEE (2015)","DOI":"10.1109\/ICCV.2015.36"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Tekalp, A.M.K., Sezan, M.I.: Hight-resolution image reconstruction from lower-resolution image sequences and space varying image restoration. In: Proceedings of the IEEE International Conference on Acoustics. Speech and Signal Processing, San Francisco, pp. 169\u2013172 (1992)","DOI":"10.1109\/ICASSP.1992.226249"},{"key":"31_CR18","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1002\/ima.10033","volume":"12","author":"Y Lu","year":"2002","unstructured":"Lu, Y., Imanura, M.: Pyramid-based super-resolution of the under sampled and subpixel shifted image sequence. Int. J. Syst. Technol. 12, 254\u2013263 (2002)","journal-title":"Int. J. Syst. Technol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62463-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T08:12:15Z","timestamp":1607933535000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62463-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030624620","9783030624637"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62463-7_31","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":"11 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"360","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":"118","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":"40","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":"33% - 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":"2.2","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}