{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:50:38Z","timestamp":1743007838210,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031202322"},{"type":"electronic","value":"9783031202339"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20233-9_55","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:02:48Z","timestamp":1667433768000},"page":"541-549","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low-illumination Palmprint Image Enhancement Based on U-Net Neural Network"],"prefix":"10.1007","author":[{"given":"Kaijun","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Duojie","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xiancheng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Guangnan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"55_CR1","doi-asserted-by":"crossref","unstructured":"Jian, M., Yin, Y., Dong, J., Zhang, W.: Comprehensive assessment of non-uniform illumination for 3D heightmap reconstruction in outdoor environments. Comput. Ind. 99, 110\u2013118 (2018)","DOI":"10.1016\/j.compind.2018.03.034"},{"issue":"1","key":"55_CR2","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.dsp.2010.09.002","volume":"21","author":"GG Bhutada","year":"2011","unstructured":"Bhutada, G.G., Anand, R.S., Saxena, S.C.: Edge preserved image enhancement using adaptive fusion of miages denoised by wavelet and cruvelet transform. Dig. Sig. Process. 21(1), 118\u2013130 (2011)","journal-title":"Dig. Sig. Process."},{"key":"55_CR3","doi-asserted-by":"publisher","unstructured":"Zotin, A.: Fast algorithm of image enhancement based onmulti-scale retinex. Procedia Comput. Sci. 131, 6\u201314 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.04.179","DOI":"10.1016\/j.procs.2018.04.179"},{"key":"55_CR4","doi-asserted-by":"publisher","unstructured":"Sun, S.S., et al.: An adaptive segmentationmethod combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment. Inform. Process. Agric. 6(2), 200\u2013215 (2019). https:\/\/doi.org\/10.1016\/j.inpa.2018.08.011","DOI":"10.1016\/j.inpa.2018.08.011"},{"key":"55_CR5","doi-asserted-by":"crossref","unstructured":"Lorek, G., Akintayo, A., Sarkar, S.: LLNet:a deep autoencoder approach to natural low-Light image enhancement. Pattern Recogn. 61, 650\u2013662 (2017)","DOI":"10.1016\/j.patcog.2016.06.008"},{"key":"55_CR6","doi-asserted-by":"crossref","unstructured":"Wang G, Kang W, Wu Q, et al. Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition[C]\/\/2018 Digital Image Computing: Techniques and Applications (DICTA).2018","DOI":"10.1109\/DICTA.2018.8615782"},{"key":"55_CR7","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: ANU-Net: attention-based Nested U-Net to exploit full resolution features for medical image segmentation. Comput. Graph. 90, 11\u201320 (2020)","DOI":"10.1016\/j.cag.2020.05.003"},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Zhimeng, H., Muwei, J., Gai-Ge, W.: ConvUNeXt: an efficient convolution neural network for medical image segmentation. Knowl.-Based Syst. 253, 109512 (2022)","DOI":"10.1016\/j.knosys.2022.109512"},{"key":"55_CR9","doi-asserted-by":"crossref","unstructured":"Keles, O., et al.: On the computation of PSNR for a set of images or video (2021)","DOI":"10.1109\/PCS50896.2021.9477470"},{"issue":"12","key":"55_CR10","first-page":"1758","volume":"2006","author":"YB Tong","year":"2006","unstructured":"Tong, Y.B., Zhang, Q.S., Qi, Y.P.: Image quality assessing by combining PSNR with SSIM. J. Image Graph. 2006(12), 1758\u20131763 (2006)","journal-title":"J. Image Graph."},{"key":"55_CR11","doi-asserted-by":"publisher","unstructured":"Tome, P., Marcel, S.: On the vulnerability of palm vein recognition to spoofing attacks. In: IAPR International Conference on Biometrics (ICB) (2015). https:\/\/doi.org\/10.1109\/ICB.2015.7139056. https:\/\/publications.idiap.ch\/index.php\/publications\/show\/3096","DOI":"10.1109\/ICB.2015.7139056"},{"key":"55_CR12","unstructured":"CASIA Palmprint Database. https:\/\/biometrics.idealtest.org"},{"key":"55_CR13","unstructured":"IITDelhiTouchlessPalmprintDatabase. https:\/\/www4.comp.polyu.edu.hk\/csajaykr\/IITD\/Database_Palm.htmlcsajaykr\/IITD\/Database_Palm.html"},{"key":"55_CR14","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K.: Contrast limited adaptive histogram equalization. Graph. Gems, 474\u2013485 (1994)","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"55_CR15","doi-asserted-by":"crossref","unstructured":"Grossmann, J.A., et al.: Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15(4), 0515056 (1984)","DOI":"10.1137\/0515056"},{"key":"55_CR16","doi-asserted-by":"crossref","unstructured":"Daniel J,J.: Retinex processing for automatic image enhancement. J. Electron. Imag. 13(1), 100\u2013110 (2004)","DOI":"10.1117\/1.1636183"},{"key":"55_CR17","doi-asserted-by":"crossref","unstructured":"Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 71(2), 319\u2013392 (2009)","DOI":"10.1111\/j.1467-9868.2008.00700.x"},{"key":"55_CR18","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"},{"key":"55_CR19","unstructured":"Alom, M.Z., et al.: The history began from alexnet: a comprehensive survey on deep learning approaches (2018)"},{"key":"55_CR20","unstructured":"Simonyan, K, Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"55_CR21","doi-asserted-by":"crossref","unstructured":"Kong, W.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: International Conference on Pattern Recognition. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1334184"},{"key":"55_CR22","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., et al. Reducing the Dimensionality of Data with Neural Networks. Science 313(5786), 504\u2013507 (2006)","DOI":"10.1126\/science.1127647"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20233-9_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:33:31Z","timestamp":1667435611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20233-9_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031202322","9783031202339"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20233-9_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/","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":"115","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":"70","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":"61% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}