{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T08:34:17Z","timestamp":1777538057449,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"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_24","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:03:14Z","timestamp":1631275394000},"page":"295-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Non-iterative Phase Retrieval with Cascaded Neural Networks"],"prefix":"10.1007","author":[{"given":"Tobias","family":"Uelwer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Hoffmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Harmeling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"issue":"4","key":"24_CR1","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1145\/146370.146374","volume":"24","author":"LG Brown","year":"1992","unstructured":"Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. (CSUR) 24(4), 325\u2013376 (1992)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"24_CR2","unstructured":"Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., Ha, D.: Deep learning for classical Japanese literature. arXiv preprint arXiv:1812.01718 (2018)"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966217"},{"issue":"15","key":"24_CR4","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1364\/AO.21.002758","volume":"21","author":"JR Fienup","year":"1982","unstructured":"Fienup, J.R.: Phase retrieval algorithms: a comparison. Appl. Opt. 21(15), 2758\u20132769 (1982)","journal-title":"Appl. Opt."},{"key":"24_CR5","first-page":"275","volume":"231","author":"JR Fienup","year":"1987","unstructured":"Fienup, J.R., Dainty, J.C.: Phase retrieval and image reconstruction for astronomy. Image Recovery Theory Appl. 231, 275 (1987)","journal-title":"Image Recovery Theory Appl."},{"key":"24_CR6","first-page":"237","volume":"35","author":"RW Gerchberg","year":"1972","unstructured":"Gerchberg, R.W.: A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 35, 237\u2013246 (1972)","journal-title":"Optik"},{"key":"24_CR7","unstructured":"Hand, P., Leong, O., Voroninski, V.: Phase retrieval under a generative prior. In: Advances in Neural Information Processing Systems, pp. 9136\u20139146 (2018)"},{"key":"24_CR8","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"issue":"20","key":"24_CR9","doi-asserted-by":"publisher","first-page":"5422","DOI":"10.1364\/AO.58.005422","volume":"58","author":"\u00c7 I\u015f\u0131l","year":"2019","unstructured":"I\u015f\u0131l, \u00c7., Oktem, F.S., Ko\u00e7, A.: Deep iterative reconstruction for phase retrieval. Appl. Opt. 58(20), 5422\u20135431 (2019)","journal-title":"Appl. Opt."},{"key":"24_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"11","key":"24_CR11","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"1","key":"24_CR12","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1088\/0266-5611\/21\/1\/004","volume":"21","author":"DR Luke","year":"2004","unstructured":"Luke, D.R.: Relaxed averaged alternating reflections for diffraction imaging. Inverse Probl. 21(1), 37 (2004)","journal-title":"Inverse Probl."},{"key":"24_CR13","unstructured":"Manekar, R., Tayal, K., Kumar, V., Sun, J.: End-to-end learning for phase retrieval (2020)"},{"key":"24_CR14","unstructured":"Metzler, C., Schniter, P., Veeraraghavan, A., Baraniuk, R.G.: prDeep: robust phase retrieval with a flexible deep network. In: International Conference on Machine Learning, pp. 3501\u20133510 (2018)"},{"issue":"3","key":"24_CR15","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1364\/JOSAA.7.000394","volume":"7","author":"RP Millane","year":"1990","unstructured":"Millane, R.P.: Phase retrieval in crystallography and optics. JOSA A 7(3), 394\u2013411 (1990)","journal-title":"JOSA A"},{"issue":"1","key":"24_CR16","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s10043-019-00574-8","volume":"27","author":"Y Nishizaki","year":"2020","unstructured":"Nishizaki, Y., Horisaki, R., Kitaguchi, K., Saito, M., Tanida, J.: Analysis of non-iterative phase retrieval based on machine learning. Opt. Rev. 27(1), 136\u2013141 (2020). https:\/\/doi.org\/10.1007\/s10043-019-00574-8","journal-title":"Opt. Rev."},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"issue":"4","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1137\/16M1102884","volume":"10","author":"Y Romano","year":"2017","unstructured":"Romano, Y., Elad, M., Milanfar, P.: The little engine that could: regularization by denoising (RED). SIAM J. Imaging Sci. 10(4), 1804\u20131844 (2017)","journal-title":"SIAM J. Imaging Sci."},{"issue":"2","key":"24_CR19","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2017","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491\u2013503 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"24_CR20","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Uelwer, T., Oberstra\u00df, A., Harmeling, S.: Phase retrieval using conditional generative adversarial networks. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 731\u2013738. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412523"},{"issue":"4","key":"24_CR22","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":"24_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Z., Sun, Y., Liu, J., Kamilov, U.: Online regularization by denoising with applications to phase retrieval. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3887\u20133895 (2019)","DOI":"10.1109\/ICCVW.2019.00482"},{"key":"24_CR24","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"issue":"9","key":"24_CR25","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1038\/nphoton.2013.187","volume":"7","author":"G Zheng","year":"2013","unstructured":"Zheng, G., Horstmeyer, R., Yang, C.: Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics 7(9), 739 (2013)","journal-title":"Nat. Photonics"}],"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_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:08:59Z","timestamp":1631275739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86340-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863395","9783030863401"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86340-1_24","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)"}}]}}