{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:47:22Z","timestamp":1743137242914,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872304"},{"type":"electronic","value":"9783030872311"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87231-1_13","type":"book-chapter","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T08:18:55Z","timestamp":1638951535000},"page":"129-139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy"],"prefix":"10.1007","author":[{"given":"Alan Q.","family":"Wang","sequence":"first","affiliation":[]},{"given":"Aaron K.","family":"LaViolette","sequence":"additional","affiliation":[]},{"given":"Leo","family":"Moon","sequence":"additional","affiliation":[]},{"given":"Chris","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Mert R.","family":"Sabuncu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1109\/TCI.2020.3006727","volume":"6","author":"CD Bahadir","year":"2020","unstructured":"Bahadir, C.D., Wang, A.Q., Dalca, A.V., Sabuncu, M.R.: Deep-learning-based optimization of the under-sampling pattern in MRI. IEEE Trans. Comput. Imaging 6, 1139\u20131152 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"1","key":"13_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183\u2013202 (2009)","journal-title":"SIAM J. Imaging Sci."},{"issue":"1","key":"13_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends Mach. Learn."},{"key":"13_CR4","unstructured":"Chakrabarti, A.: Learning sensor multiplexing design through back-propagation. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 3089\u20133097. Curran Associates Inc., Red Hook (2016)"},{"key":"13_CR5","unstructured":"Diamond, S., Sitzmann, V., Heide, F., Wetzstein, G.: Unrolled optimization with deep priors. CoRR abs\/1705.08041 (2017)"},{"issue":"10","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","volume":"17","author":"A Foi","year":"2008","unstructured":"Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737\u20131754 (2008)","journal-title":"IEEE Trans. Image Process."},{"issue":"19","key":"13_CR7","doi-asserted-by":"publisher","first-page":"28190","DOI":"10.1364\/OE.403195","volume":"28","author":"GM Gibson","year":"2020","unstructured":"Gibson, G.M., Johnson, S.D., Padgett, M.J.: Single-pixel imaging 12 years on: a review. Opt. Express 28(19), 28190\u201328208 (2020)","journal-title":"Opt. Express"},{"issue":"4","key":"13_CR8","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1016\/S0006-3495(01)76173-5","volume":"80","author":"A Hopt","year":"2001","unstructured":"Hopt, A., Neher, E.: Highly nonlinear photodamage in two-photon fluorescence microscopy. Biophys. J . 80(4), 2029\u20132036 (2001)","journal-title":"Biophys. J ."},{"key":"13_CR9","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparametrization with gumble-softmax. In: Proceedings of the International Conference on Learning Representations 2017. OpenReviews.net, April 2017"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Kellman, M., Bostan, E., Chen, M., Waller, L.: Data-driven design for Fourier ptychographic microscopy. In: 2019 IEEE International Conference on Computational Photography (ICCP), pp. 1\u20138 (2019)","DOI":"10.1109\/ICCPHOT.2019.8747339"},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neunet.2020.01.026","volume":"125","author":"S Lee","year":"2020","unstructured":"Lee, S., Negishi, M., Urakubo, H., Kasai, H., Ishii, S.: Mu-net: multi-scale U-net for two-photon microscopy image denoising and restoration. Neural Netw. 125, 92\u2013103 (2020)","journal-title":"Neural Netw."},{"key":"13_CR12","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1038\/nmeth817","volume":"2","author":"J Lichtman","year":"2005","unstructured":"Lichtman, J., Conchello, J.: Fluorescence microscopy. Nat. Methods 2, 910\u2013919 (2005)","journal-title":"Nat. Methods"},{"key":"13_CR13","unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. In: International Conference on Learning Representations (2017)"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Magidson, V., Khodjakov, A.: Circumventing photodamage in live-cell microscopy. In: Sluder, G., Wolf, D.E. (eds.) Digital Microscopy, Methods in Cell Biology, vol. 114, pp. 545\u2013560. Academic Press (2013)","DOI":"10.1016\/B978-0-12-407761-4.00023-3"},{"issue":"1","key":"13_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TIP.2012.2202675","volume":"22","author":"M Makitalo","year":"2013","unstructured":"Makitalo, M., Foi, A.: Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise. IEEE Trans. Image Process. 22(1), 91\u2013103 (2013)","journal-title":"IEEE Trans. Image Process."},{"issue":"14","key":"13_CR16","doi-asserted-by":"publisher","first-page":"144001","DOI":"10.1088\/1361-6463\/aafe88","volume":"52","author":"VJ Parot","year":"2019","unstructured":"Parot, V.J., et al.: Compressed Hadamard microscopy for high-speed optically sectioned neuronal activity recordings. J. Phys. D Appl. Phys. 52(14), 144001 (2019)","journal-title":"J. Phys. D Appl. Phys."},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"13_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201333","volume":"37","author":"V Sitzmann","year":"2018","unstructured":"Sitzmann, V., et al.: End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37(4), 1\u201313 (2018)","journal-title":"ACM Trans. Graph."},{"issue":"11","key":"13_CR19","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1364\/AO.48.002078","volume":"48","author":"L Streeter","year":"2009","unstructured":"Streeter, L., Burling-Claridge, G.R., Cree, M.J., K\u00fcnnemeyer, R.: Optical full Hadamard matrix multiplexing and noise effects. Appl. Opt. 48(11), 2078\u20132085 (2009)","journal-title":"Appl. Opt."},{"issue":"26","key":"13_CR20","doi-asserted-by":"publisher","first-page":"E1679","DOI":"10.1073\/pnas.1119511109","volume":"109","author":"V Studer","year":"2012","unstructured":"Studer, V., Bobin, J., Chahid, M., Mousavi, H.S., Candes, E., Dahan, M.: Compressive fluorescence microscopy for biological and hyperspectral imaging. Proc. Natl. Acad. Sci. 109(26), E1679\u2013E1687 (2012)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Sun, H., Dalca, A.V., Bouman, K.L.: Learning a probabilistic strategy for computational imaging sensor selection. In: 2020 IEEE International Conference on Computational Photography (ICCP), pp. 1\u201312 (2020)","DOI":"10.1109\/ICCP48838.2020.9105133"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Sun, M.J., Meng, L.T., Edgar, M.: A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging. Sci. Rep. 7 (2017). Article number: 3464","DOI":"10.1038\/s41598-017-03725-6"},{"key":"13_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-61598-7_3","volume-title":"Machine Learning for Medical Image Reconstruction","author":"AQ Wang","year":"2020","unstructured":"Wang, A.Q., Dalca, A.V., Sabuncu, M.R.: Neural network-based reconstruction in compressed sensing MRI without fully-sampled training data. In: Deeba, F., Johnson, P., W\u00fcrfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 27\u201337. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61598-7_3"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/s41592-018-0239-0","volume":"16","author":"H Wang","year":"2019","unstructured":"Wang, H., Rivenson, Y., Jin, Y.: Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103\u2013110 (2019)","journal-title":"Nat. Methods"},{"issue":"4","key":"13_CR25","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. Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"Trans. Image Process."},{"issue":"12","key":"13_CR26","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1038\/s41592-018-0216-7","volume":"15","author":"M Weigert","year":"2018","unstructured":"Weigert, M., Schmidt, U., Boothe, T.: Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15(12), 1090\u20131097 (2018)","journal-title":"Nat. Methods"},{"issue":"20","key":"13_CR27","doi-asserted-by":"publisher","first-page":"4981","DOI":"10.1364\/OL.44.004981","volume":"44","author":"P Wijesinghe","year":"2019","unstructured":"Wijesinghe, P., Escobet-Montalb\u00e1n, A., Chen, M., Munro, P.R.T., Dholakia, K.: Optimal compressive multiphoton imaging at depth using single-pixel detection. Opt. Lett. 44(20), 4981 (2019)","journal-title":"Opt. Lett."},{"issue":"11","key":"13_CR28","doi-asserted-by":"publisher","first-page":"2632","DOI":"10.1109\/TMI.2019.2907093","volume":"38","author":"Y Xue","year":"2019","unstructured":"Xue, Y., Bigras, G., Hugh, J., Ray, N.: Training convolutional neural networks and compressed sensing end-to-end for microscopy cell detection. IEEE Trans. Med. Imaging 38(11), 2632\u20132641 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR29","unstructured":"Yang, Y., Sun, J., Li, H., Xu, Z.: Deep ADMM-Net for compressive sensing MRI. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Yao, R., Ochoa, M., Yan, P.: Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing - a deep learning approach. Light Sci. Appl. 8 (2019). Article number: 26","DOI":"10.1038\/s41377-019-0138-x"},{"key":"13_CR31","doi-asserted-by":"publisher","first-page":"55773","DOI":"10.1109\/ACCESS.2020.2981505","volume":"8","author":"X Yu","year":"2020","unstructured":"Yu, X., Yang, F., Gao, B., Ran, J., Huang, X.: Deep compressive single pixel imaging by reordering Hadamard basis: a comparative study. IEEE Access 8, 55773\u201355784 (2020)","journal-title":"IEEE Access"},{"key":"13_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-61598-7_9","volume-title":"Machine Learning for Medical Image Reconstruction","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Extending LOUPE for K-space under-sampling pattern optimization in multi-coil MRI. In: Deeba, F., Johnson, P., W\u00fcrfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 91\u2013101. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61598-7_9"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A Poisson-Gaussian denoising dataset with real fluorescence microscopy images. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11702\u201311710 (2019)","DOI":"10.1109\/CVPR.2019.01198"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87231-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T20:02:45Z","timestamp":1698955365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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":"The conference was held virtually.","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)"}}]}}