{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:58:06Z","timestamp":1743065886982,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031319747"},{"type":"electronic","value":"9783031319754"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-31975-4_2","type":"book-chapter","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T23:30:02Z","timestamp":1683761402000},"page":"16-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Neural Generation of\u00a04K Masks for\u00a0Homogeneous Diffusion Inpainting"],"prefix":"10.1007","author":[{"given":"Karl","family":"Schrader","sequence":"first","affiliation":[]},{"given":"Pascal","family":"Peter","sequence":"additional","affiliation":[]},{"given":"Niklas","family":"K\u00e4mper","sequence":"additional","affiliation":[]},{"given":"Joachim","family":"Weickert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"2_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 2017 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, vol. 1, pp. 1122\u20131131 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-031-04881-4_42","volume-title":"Pattern Recognition and Image Analysis","author":"T Alt","year":"2022","unstructured":"Alt, T., Peter, P., Weickert, J.: Learning sparse masks for diffusion-based image inpainting. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., S\u00e1nchez, J.A. (eds.) IbPRIA 2022. LNCS, vol. 13256, pp. 528\u2013539. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-04881-4_42"},{"issue":"5","key":"2_CR3","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2011","unstructured":"Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898\u2013916 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"2_CR4","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1137\/080716396","volume":"70","author":"Z Belhachmi","year":"2009","unstructured":"Belhachmi, Z., Bucur, D., Burgeth, B., Weickert, J.: How to choose interpolation data in images. SIAM J. Appl. Math. 70(1), 333\u2013352 (2009)","journal-title":"SIAM J. Appl. Math."},{"issue":"5","key":"2_CR5","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa5bfd","volume":"33","author":"S Bonettini","year":"2017","unstructured":"Bonettini, S., Loris, I., Porta, F., Prato, M., Rebegoldi, S.: On the convergence of a linesearch based proximal-gradient method for nonconvex optimization. Inverse Prob. 33(5), 055005 (2017)","journal-title":"Inverse Prob."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/0165-1684(88)90028-X","volume":"15","author":"S Carlsson","year":"1988","unstructured":"Carlsson, S.: Sketch based coding of grey level images. Signal Process. 15, 57\u201383 (1988)","journal-title":"Signal Process."},{"key":"2_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-030-89131-2_40","volume-title":"Computer Analysis of Images and Patterns Part 2","author":"V Chizhov","year":"2021","unstructured":"Chizhov, V., Weickert, J.: Efficient data optimisation for harmonic inpainting with finite elements. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds.) CAIP 2021. LNCS, vol. 13053, pp. 432\u2013441. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-89131-2_40"},{"issue":"10","key":"2_CR8","doi-asserted-by":"publisher","first-page":"2564","DOI":"10.1109\/TMM.2019.2958760","volume":"22","author":"Q Dai","year":"2019","unstructured":"Dai, Q., Chopp, H., Pouyet, E., Cossairt, O., Walton, M., Katsaggelos, A.K.: Adaptive image sampling using deep learning and its application on X-ray fluorescence image reconstruction. IEEE Trans. Multimedia 22(10), 2564\u20132578 (2019)","journal-title":"IEEE Trans. Multimedia"},{"issue":"4","key":"2_CR9","first-page":"1669","volume":"14","author":"V Daropoulos","year":"2021","unstructured":"Daropoulos, V., Augustin, M., Weickert, J.: Sparse inpainting with smoothed particle hydrodynamics. SIAM J. Appl. Math. 14(4), 1669\u20131704 (2021)","journal-title":"SIAM J. Appl. Math."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2_CR11","unstructured":"Floyd, R.W., Steinberg, L.: An adaptive algorithm for spatial grey scale. In: Proceedings of the Society of Information Display, vol. 17, pp. 75\u201377 (1976)"},{"issue":"2\u20133","key":"2_CR12","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s10851-008-0087-0","volume":"31","author":"I Gali\u0107","year":"2008","unstructured":"Gali\u0107, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., Seidel, H.P.: Image compression with anisotropic diffusion. J. Math. Imaging Vis. 31(2\u20133), 255\u2013269 (2008)","journal-title":"J. Math. Imaging Vis."},{"issue":"1","key":"2_CR13","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/MSP.2013.2273004","volume":"31","author":"C Guillemot","year":"2014","unstructured":"Guillemot, C., Le Meur, O.: Image inpainting: overview and recent advances. IEEE Signal Process. Mag. 31(1), 127\u2013144 (2014)","journal-title":"IEEE Signal Process. Mag."},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-642-40395-8_12","volume-title":"Energy Minimisation Methods in Computer Vision and Pattern Recognition","author":"L Hoeltgen","year":"2013","unstructured":"Hoeltgen, L., Setzer, S., Weickert, J.: An optimal control approach to find sparse data for Laplace interpolation. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, X.C. (eds.) Energy Minimisation Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, vol. 8081, pp. 151\u2013164. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40395-8_12"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"K\u00e4mper, N., Weickert, J.: Domain decomposition algorithms for real-time homogeneous diffusion inpainting in 4K. In: Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, pp. 1680\u20131684 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746831"},{"key":"2_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations, San Diego (2015)"},{"key":"2_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-642-24785-9_3","volume-title":"Scale Space and Variational Methods in Computer Vision","author":"M Mainberger","year":"2012","unstructured":"Mainberger, M., et al.: Optimising spatial and tonal data for homogeneous diffusion inpainting. In: Bruckstein, A.M., ter Haar Romeny, B., Bronstein, A.M., Bronstein, M.M. (eds.) SSVM 2011. LNCS, vol. 6667, pp. 26\u201337. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24785-9_3"},{"issue":"2","key":"2_CR18","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1137\/130942954","volume":"7","author":"P Ochs","year":"2014","unstructured":"Ochs, P., Chen, Y., Brox, T., Pock, T.: iPiano: inertial proximal algorithm for nonconvex optimization. SIAM J. Imag. Sci. 7(2), 1388\u20131419 (2014)","journal-title":"SIAM J. Imag. Sci."},{"key":"2_CR19","volume-title":"JPEG: Still Image Data Compression Standard","author":"WB Pennebaker","year":"1992","unstructured":"Pennebaker, W.B., Mitchell, J.L.: JPEG: Still Image Data Compression Standard. Springer, New York (1992)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Peter, P.: A Wasserstein GAN for joint learning of inpainting and its spatial optimisation. arXiv:2202.05623 [eess.IV] (2022)","DOI":"10.1007\/978-3-031-26431-3_11"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Peter, P., Schrader, K., Alt, T., Weickert, J.: Deep spatial and tonal data optimisation for homogeneous diffusion inpainting. arXiv:2208.14371 [eess.IV] (2022)","DOI":"10.1007\/s10044-023-01162-y"},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/978-3-030-86340-1_46","volume-title":"Artificial Neural Networks and Machine Learning - ICANN 2021","author":"D Va\u0161ata","year":"2021","unstructured":"Va\u0161ata, D., Halama, T., Friedjungov\u00e1, M.: Image inpainting using Wasserstein generative adversarial imputation network. In: Farka\u0161, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12892, pp. 575\u2013586. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86340-1_46"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/3-540-31272-2_19","volume-title":"Visualization and Processing of Tensor Fields","author":"J Weickert","year":"2006","unstructured":"Weickert, J., Welk, M.: Tensor field interpolation with PDEs. In: Weickert, J., Hagen, H. (eds.) Visualization and Processing of Tensor Fields, pp. 315\u2013325. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/3-540-31272-2_19"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Wendland, H.: Numerical Linear Algebra: An Introduction. Cambridge University Press, Cambridge (2017)","DOI":"10.1017\/9781316544938"}],"container-title":["Lecture Notes in Computer Science","Scale Space and Variational Methods in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31975-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:17:16Z","timestamp":1710245836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31975-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031319747","9783031319754"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31975-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Scale Space and Variational Methods in Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santa Margherita di Pula","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"21 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scalespace2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eventi.unibo.it\/ssvm2023","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"57","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":"79% - 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","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)"}}]}}