{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T21:14:12Z","timestamp":1768166052312,"version":"3.49.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030863395","type":"print"},{"value":"9783030863401","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86340-1_46","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:03:14Z","timestamp":1631275394000},"page":"575-586","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Image Inpainting Using Wasserstein Generative Adversarial Imputation Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0616-4340","authenticated-orcid":false,"given":"Daniel","family":"Va\u0161ata","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-083X","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Halama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3363-294X","authenticated-orcid":false,"given":"Magda","family":"Friedjungov\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"issue":"8","key":"46_CR1","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1109\/LGRS.2017.2702106","volume":"14","author":"N Amrani","year":"2017","unstructured":"Amrani, N., Serra-Sagrist\u00e0, J., Peter, P., Weickert, J.: Diffusion-based inpainting for coding remote-sensing data. IEEE Geosci. Remote Sens. Lett. 14(8), 1203\u20131207 (2017)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"8","key":"46_CR2","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/83.935036","volume":"10","author":"C Ballester","year":"2001","unstructured":"Ballester, C., Bertalmio, M., Caselles, V., Sapiro, G., Verdera, J.: Filling-in by joint interpolation of vector fields and gray levels. IEEE Trans. Image Process. 10(8), 1200\u20131211 (2001)","journal-title":"IEEE Trans. Image Process."},{"issue":"S1","key":"46_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1017\/S1431927617001428","volume":"23","author":"A Barnum","year":"2017","unstructured":"Barnum, A., Jiao, J.: Adaptive biharmonic in-painting for sparse acquisition using variance frames. Microsc. Microanal. 23(S1), 148\u2013149 (2017). https:\/\/doi.org\/10.1017\/S1431927617001428","journal-title":"Microsc. Microanal."},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 417\u2013424 (2000)","DOI":"10.1145\/344779.344972"},{"key":"46_CR5","doi-asserted-by":"publisher","first-page":"48451","DOI":"10.1109\/ACCESS.2020.2979348","volume":"8","author":"W Cai","year":"2020","unstructured":"Cai, W., Wei, Z.: PiiGAN: generative adversarial networks for pluralistic image inpainting. IEEE Access 8, 48451\u201348463 (2020)","journal-title":"IEEE Access"},{"key":"46_CR6","unstructured":"Chen, Y., Ranftl, R., Pock, T.: A bi-level view of inpainting-based image compression. arXiv preprint arXiv:1401.4112 (2014)"},{"key":"46_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/3950312","volume":"2018","author":"SB Damelin","year":"2018","unstructured":"Damelin, S.B., Hoang, N.S.: On surface completion and image inpainting by biharmonic functions: numerical aspects. Int. J. Math. Math. Sci. 2018, 1\u20138 (2018)","journal-title":"Int. J. Math. Math. Sci."},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 341\u2013346 (2001)","DOI":"10.1145\/383259.383296"},{"key":"46_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/978-3-030-50423-6_17","volume-title":"Computational Science \u2013 ICCS 2020","author":"M Friedjungov\u00e1","year":"2020","unstructured":"Friedjungov\u00e1, M., Va\u0161ata, D., Balatsko, M., Ji\u0159ina, M.: Missing features reconstruction using a Wasserstein generative adversarial imputation network. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12140, pp. 225\u2013239. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50423-6_17"},{"key":"46_CR10","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672\u20132680. Curran Associates, Inc. (2014)"},{"key":"46_CR11","doi-asserted-by":"publisher","unstructured":"Hua, P., Liu, X., Liu, M., Dong, L., Hui, M., Zhao, Y.: Image inpainting using Wasserstein generative adversarial network. In: Optics and Photonics for Information Processing XII, vol. 10751, pp. 183\u2013194. SPIE (2018). https:\/\/doi.org\/10.1117\/12.2320212","DOI":"10.1117\/12.2320212"},{"key":"46_CR12","unstructured":"Hui, Z., Li, J., Wang, X., Gao, X.: Image fine-grained inpainting. arXiv preprint arXiv:2002.02609 (2020)"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Jam, J., Kendrick, C., Drouard, V., Walker, K., Hsu, G.S., Yap, M.H.: Symmetric skip connection Wasserstein GAN for high-resolution facial image inpainting. arXiv preprint arXiv:2001.03725 (2020)","DOI":"10.5220\/0010188700350044"},{"key":"46_CR14","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"46_CR16","doi-asserted-by":"publisher","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.278","DOI":"10.1109\/cvpr.2016.278"},{"key":"46_CR17","unstructured":"Rubner, Y., Guibas, L.J., Tomasi, C.: The earth mover\u2019s distance, multi-dimensional scaling, and color-based image retrieval. In: Proceedings of the ARPA Image Understanding Workshop, vol. 661, p. 668 (1997)"},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"Shin, Y.G., Sagong, M.C., Yeo, Y.J., Kim, S.W., Ko, S.J.: Pepsi++: fast and lightweight network for image inpainting. IEEE Trans. Neural Netw. Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.2978501"},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Simakov, D., Caspi, Y., Shechtman, E., Irani, M.: Summarizing visual data using bidirectional similarity. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587842"},{"issue":"1","key":"46_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","volume":"9","author":"A Telea","year":"2004","unstructured":"Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tools 9(1), 23\u201334 (2004). https:\/\/doi.org\/10.1080\/10867651.2004.10487596","journal-title":"J. Graph. Tools"},{"key":"46_CR21","unstructured":"Villani, C.: Optimal Transport: Old and New, vol. 338. Springer, Heidelberg (2008)"},{"issue":"1","key":"46_CR22","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98\u2013117 (2009)","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"46_CR23","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."},{"key":"46_CR24","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. CoRR abs\/1511.07122 (2016)"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5505\u20135514 (2018)","DOI":"10.1109\/CVPR.2018.00577"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, C., Cham, T.J., Cai, J.: Pluralistic image completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438\u20131447 (2019)","DOI":"10.1109\/CVPR.2019.00153"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86340-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:14:40Z","timestamp":1631276080000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86340-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863395","9783030863401"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86340-1_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"2.5","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)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","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)"}}]}}