{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T17:36:51Z","timestamp":1772127411161,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031554704","type":"print"},{"value":"9783031554711","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\/978-3-031-55471-1_9","type":"book-chapter","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T05:02:22Z","timestamp":1710565342000},"page":"108-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Improved 4D Convolutional Neural Network for\u00a0Light Field Reconstruction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4700-0844","authenticated-orcid":false,"given":"Qiuming","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7852-1779","authenticated-orcid":false,"given":"Ruiqin","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3756-878X","authenticated-orcid":false,"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6895-2882","authenticated-orcid":false,"given":"Yichen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,17]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Shin, C., Jeon, H.-G., Yoon, Y., Kweon, I.S., Kim, S. J.: EPINET: a fully-convolutional neural network using epipolar geometry for depth from light field images. In Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 4748\u20134757 (2018)","DOI":"10.1109\/CVPR.2018.00499"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Mitra, K., Veeraraghavan, A.: Light field denoising, light field superresolution and stereo camera based refocussing using a GMM light field patch prior. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition Workshops, pp. 22\u201328 (2012)","DOI":"10.1109\/CVPRW.2012.6239346"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Yucer, K., Sorkine-Hornung, A., Wang, O., Sorkine-Hornung, O.: Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction. ACM Trans. Graph. 35(3), 22:1\u201322:15 (2016)","DOI":"10.1145\/2876504"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Kim, C., Zimmer, H., Pritch, Y., Sorkine-Hornung, A., Gross, M.: Scene reconstruction from high spatio-angular resolution light fields. ACM Trans. Graph. 32(4), 73:1\u201373:12 (2013)","DOI":"10.1145\/2461912.2461926"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Wang, T.-C., Efros, A.A., Ramamoorthi, R.: Occlusion-awaredepth estimation using light-field cameras. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3487\u20133495 (2015)","DOI":"10.1109\/ICCV.2015.398"},{"issue":"9","key":"9_CR6","doi-asserted-by":"publisher","first-page":"3405","DOI":"10.1109\/TIP.2013.2268939","volume":"22","author":"J Pearson","year":"2013","unstructured":"Pearson, J., Brookes, M., Dragotti, P.L.: Plenoptic layer-based modeling for image based rendering. IEEE Trans. Image Process. 22(9), 3405\u20133419 (2013)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, Y., Dai, Q.: Light field from micro-baseline image pair. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 3800\u20133809 (2015)","DOI":"10.1109\/CVPR.2015.7299004"},{"issue":"5","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TVCG.2016.2532329","volume":"23","author":"F-L Zhang","year":"2017","unstructured":"Zhang, F.-L., et al.: PlenoPatch: patch-based plenoptic image manipulation. IEEE Trans. Visualization Comput. Graph. 23(5), 1561\u20131573 (2017). https:\/\/doi.org\/10.1109\/TVCG.2016.2532329","journal-title":"IEEE Trans. Visualization Comput. Graph."},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chai, J.X., Tong, X., Chan, S.C., et al.: Plenoptic sampling. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 307\u2013318 (2000)","DOI":"10.1145\/344779.344932"},{"issue":"11","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/TCSVT.2003.817350","volume":"13","author":"C Zhang","year":"2003","unstructured":"Zhang, C., Chen, T.: Spectral analysis for sampling image-based rendering data. IEEE Trans. Circuits Syst. Video Technol. 13(11), 1038\u20131050 (2003)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"9_CR11","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1109\/TIP.2011.2163895","volume":"21","author":"MN Do","year":"2011","unstructured":"Do, M.N., Marchand-Maillet, D., Vetterli, M.: On the bandwidth of the plenoptic function. IEEE Trans. Image Process. 21(2), 708\u2013717 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/s00530-016-0515-8","volume":"23","author":"CJ Zhu","year":"2017","unstructured":"Zhu, C.J., Yu, L.: Spectral analysis of image-based rendering data with scene geometry. Multimedia Syst. 23, 627\u2013644 (2017)","journal-title":"Multimedia Syst."},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/TPAMI.2017.2653101","volume":"40","author":"S Vagharshakyan","year":"2018","unstructured":"Vagharshakyan, S., Bregovic, R., Gotchev, A.: Light field reconstruction using shearlet transform. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 133\u2013147 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103697","volume":"130","author":"W Chen","year":"2022","unstructured":"Chen, W., Zhu, C.: Spectral analysis of a surface occlusion model for image-based rendering sampling. Digital Signal Process. 130, 103697 (2022)","journal-title":"Digital Signal Process."},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y., So Kweon, I.: Learning a deep convolutional network for light-field image superresolution. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 24\u201332 (2015)","DOI":"10.1109\/ICCVW.2015.17"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Flynn, J., Neulander, I., Philbin, J., Snavely, N.: DeepStereo: learning to predict new views from the world\u2019s imagery. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 5515\u20135524 (2016)","DOI":"10.1109\/CVPR.2016.595"},{"issue":"7","key":"9_CR17","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TPAMI.2018.2845393","volume":"41","author":"G Wu","year":"2019","unstructured":"Wu, G., Liu, Y., Fang, L., Dai, Q., Chai, T.: Light field reconstruction using convolutional network on EPI and extended applications. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1681\u20131694 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"9_CR18","doi-asserted-by":"publisher","first-page":"3261","DOI":"10.1109\/TIP.2019.2895463","volume":"28","author":"G Wu","year":"2019","unstructured":"Wu, G., Liu, Y., Dai, Q., Chai, T.: Learning sheared EPI structure for light field reconstruction. IEEE Trans. Image Process. 28(7), 3261\u20133273 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, F., Wang, Z., Hou, G., Sun, Z., Tan, T.: End-to-end view synthesis for light field imaging with Pseudo 4DCNN. In: Proceedings of European Conference on Computer Vision, pp. 333\u2013348 (2018)","DOI":"10.1007\/978-3-030-01216-8_21"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Yeung, W.F.H., Hou, J., Chen, J., Chung, Y.Y., Chen, X.: Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues. In: Proceedings of European Conference on Computer Vision, pp. 137\u2013152 (2018)","DOI":"10.1007\/978-3-030-01231-1_9"},{"issue":"3","key":"9_CR21","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1109\/TPAMI.2019.2945027","volume":"43","author":"N Meng","year":"2019","unstructured":"Meng, N., So, H.K.H., Sun, X., et al.: High-dimensional dense residual convolutional neural network for light field reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 873\u2013886 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR22","unstructured":"Raj, S., Lowney, M., Shah, R., Wetzstein, G.: Stanford lytro light field archive (2016). http:\/\/lightfields.stanford.edu\/LF2016.html."},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Honauer, K., Johannsen, O., Kondermann, D., Goldluecke, B.: A dataset and evaluation methodology for depth estimation on 4D light fields. In: Proceedings of Asian Conference on Computer Vision, pp. 19\u201334 (2016)","DOI":"10.1007\/978-3-319-54187-7_2"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile Networks and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-55471-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T11:58:41Z","timestamp":1731585521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-55471-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031554704","9783031554711"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-55471-1_9","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MONAMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile Networks and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yingtan","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":"27 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"monami2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mon-ami.eai-conferences.org\/2023\/","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":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","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":"21","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":"51% - 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":"10.5","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)"}}]}}