{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:24:05Z","timestamp":1777656245057,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250682","type":"print"},{"value":"9783031250699","type":"electronic"}],"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\/978-3-031-25069-9_33","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:15:46Z","timestamp":1676333746000},"page":"503-518","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["N2V2 - Fixing Noise2Void Checkerboard Artifacts with\u00a0Modified Sampling Strategies and\u00a0a\u00a0Tweaked Network Architecture"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-1282","authenticated-orcid":false,"given":"Eva","family":"H\u00f6ck","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6953-8915","authenticated-orcid":false,"given":"Tim-Oliver","family":"Buchholz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anselm","family":"Brachmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-5812","authenticated-orcid":false,"given":"Florian","family":"Jug","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9041-1334","authenticated-orcid":false,"given":"Alexander","family":"Freytag","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"33_CR1","unstructured":"Batson, J., Royer, L.: Noise2self: Blind denoising by self-supervision. In: International Conference on Machine Learning (ICML), pp. 524\u2013533. PMLR (2019)"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Buchholz, T.O., Jordan, M., Pigino, G., Jug, F.: Cryo-care: content-aware image restoration for cryo-transmission electron microscopy data. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 502\u2013506. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759519"},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/bs.mcb.2019.05.001","volume":"152","author":"TO Buchholz","year":"2019","unstructured":"Buchholz, T.O., Krull, A., Shahidi, R., Pigino, G., J\u00e9kely, G., Jug, F.: Content-aware image restoration for electron microscopy. Methods Cell Biol. 152, 277\u2013289 (2019)","journal-title":"Methods Cell Biol."},{"key":"33_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1007\/978-3-030-66415-2_21","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"T-O Buchholz","year":"2020","unstructured":"Buchholz, T.-O., Prakash, M., Schmidt, D., Krull, A., Jug, F.: DenoiSeg: joint denoising and segmentation. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12535, pp. 324\u2013337. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66415-2_21"},{"issue":"1","key":"33_CR5","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1038\/s41467-021-22518-0","volume":"12","author":"L von Chamier","year":"2021","unstructured":"von Chamier, L., et al.: Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat. Commun. 12(1), 2276 (2021)","journal-title":"Nat. Commun."},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Krull, A., Buchholz, T.O., Jug, F.: Noise2void-learning denoising from single noisy images. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2129\u20132137 (2019)","DOI":"10.1109\/CVPR.2019.00223"},{"key":"33_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3389\/fcomp.2020.00005","volume":"2","author":"A Krull","year":"2020","unstructured":"Krull, A., Vi\u010dar, T., Prakash, M., Lalit, M., Jug, F.: Probabilistic noise2void: unsupervised content-aware denoising. Frontiers Comput. Sci. 2, 5 (2020)","journal-title":"Frontiers Comput. Sci."},{"key":"33_CR8","unstructured":"Laine, S., Karras, T., Lehtinen, J., Aila, T.: High-quality self-supervised deep image denoising. In: Advances in Neural Information Processing Systems (NeurIPS) 32 (2019)"},{"key":"33_CR9","unstructured":"Lehtinen, J., et al.: Noise2noise: learning image restoration without clean data. arXiv preprint arXiv:1803.04189 (2018)"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez de la Morena, J., et al.: ScipionTomo: towards cryo-electron tomography software integration, reproducibility, and validation. J. Struct. Biol. 214(3), 107872 (2022)","DOI":"10.1016\/j.jsb.2022.107872"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Ouyang, W., et al.: BioImage model zoo: a community-driven resource for accessible deep learning in BioImage analysis, June 2022","DOI":"10.1101\/2022.06.07.495102"},{"key":"33_CR12","unstructured":"Prakash, M., Delbracio, M., Milanfar, P., Jug, F.: Interpretable unsupervised diversity denoising and artefact removal. In: International Conference on Learning Representations (ICLR) (2022)"},{"key":"33_CR13","unstructured":"Prakash, M., Krull, A., Jug, F.: Fully unsupervised diversity denoising with convolutional variational autoencoders. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Prakash, M., Lalit, M., Tomancak, P., Krull, A., Jug, F.: Fully unsupervised probabilistic noise2void. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2020)","DOI":"10.1109\/ISBI45749.2020.9098612"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Schroeder, A.B., Dobson, E.T.A., Rueden, C.T., et al.: The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Proteins (2021)","DOI":"10.1002\/pro.3993"},{"key":"33_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-319-66185-8_15","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"M Weigert","year":"2017","unstructured":"Weigert, M., Royer, L., Jug, F., Myers, G.: Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 126\u2013134. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_15"},{"issue":"12","key":"33_CR18","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1038\/s41592-018-0216-7","volume":"15","author":"M Weigert","year":"2018","unstructured":"Weigert, M., et al.: Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15(12), 1090\u20131097 (2018)","journal-title":"Nat. Methods"},{"issue":"7","key":"33_CR19","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. (TIP) 26(7), 3142\u20133155 (2017)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"33_CR20","unstructured":"Zhang, R.: Making convolutional networks shift-invariant again. In: International Conference on Machine Learning (ICML), pp. 7324\u20137334. PMLR (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25069-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:56:41Z","timestamp":1709816201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25069-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250682","9783031250699"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25069-9_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}