{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:21:23Z","timestamp":1778894483329,"version":"3.51.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"45-46","license":[{"start":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T00:00:00Z","timestamp":1574294400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T00:00:00Z","timestamp":1574294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No. 2016YFC0100800"],"award-info":[{"award-number":["No. 2016YFC0100800"]}]},{"name":"National Key Research and Development Program of China","award":["No. 2016YFC0100800"],"award-info":[{"award-number":["No. 2016YFC0100800"]}]},{"name":"National Key Research and Development Program of China","award":["No. 2016YFC0100800"],"award-info":[{"award-number":["No. 2016YFC0100800"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFC0100802"],"award-info":[{"award-number":["2016YFC0100802"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFC0100802"],"award-info":[{"award-number":["2016YFC0100802"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFC0100802"],"award-info":[{"award-number":["2016YFC0100802"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s11042-019-08143-6","type":"journal-article","created":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T11:03:08Z","timestamp":1574334188000},"page":"33711-33733","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Gibbs-ringing artifact suppression with knowledge transfer from natural images to MR images"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-2414","authenticated-orcid":false,"given":"Xiaole","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huali","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueming","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"key":"8143_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson E, Timofte R (2017) NTIRE 2017 challenge on single image super-resolution: dataset and study. In: 2017 IEEE conference on computer vision and pattern recognition workshops, pp 1122\u20131131","DOI":"10.1109\/CVPRW.2017.150"},{"issue":"4","key":"8143_CR2","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1109\/TMI.2002.1000255","volume":"21","author":"R Archibald","year":"2002","unstructured":"Archibald R, Gelb A (2002) A method to reduce the gibbs ringing artifact in mri scans while keeping tissue boundary integrity. IEEE Trans Med Imaging 21(4):305\u2013319","journal-title":"IEEE Trans Med Imaging"},{"key":"8143_CR3","doi-asserted-by":"crossref","unstructured":"Bae W, Yoo J, Ye JC (2017) Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification. arXiv:1611.06345 [cs.CV]","DOI":"10.1109\/CVPRW.2017.152"},{"issue":"8","key":"8143_CR4","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1109\/TIP.2003.815261","volume":"12","author":"M Bertalmio","year":"2003","unstructured":"Bertalmio M, Vese L, Sapiro G, Osher S (2003) Simultaneous structure and texture image inpainting. IEEE Trans Image Process 12(8):882\u2013889","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"8143_CR5","first-page":"417","volume":"4","author":"M Bertalmio","year":"2005","unstructured":"Bertalmio M, Sapiro G, Caselles V, Ballester C (2005) Image inpainting. Siggraph 4(9):417\u2013424","journal-title":"Siggraph"},{"issue":"2","key":"8143_CR6","doi-asserted-by":"crossref","first-page":"184123","DOI":"10.1155\/2008\/184123","volume":"2008","author":"KT Block","year":"2008","unstructured":"Block KT, Uecker M, Frahm J (2008) Suppression of mri truncation artifacts using total variation constrained data extrapolation. Int J Biomed Imaging 2008(2):184123","journal-title":"Int J Biomed Imaging"},{"issue":"1","key":"8143_CR7","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.jcp.2004.10.008","volume":"204","author":"JP Boyd","year":"2005","unstructured":"Boyd JP (2005) Trouble with gegenbauer reconstruction for defeating gibbs\u2019 phenomenon: Runge phenomenon in the diagonal limit of gegenbauer polynomial approximations. J Comput Phys 204(1):253\u2013264","journal-title":"J Comput Phys"},{"key":"8143_CR8","unstructured":"Carroll J, Carlson N, Kenyon GT (2017) Phase transitions in image denoising via sparsely coding convolutional neural networks. arXiv:1710.09875 [cs.NE]"},{"issue":"1","key":"8143_CR9","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1002\/mrm.1910170115","volume":"17","author":"RT Constable","year":"1991","unstructured":"Constable RT, Henkelman RM (1991) Data extrapolation for truncation artifact removal. Magn Reson Med 17(1):108\u2013118","journal-title":"Magn Reson Med"},{"issue":"2","key":"8143_CR10","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/TIP.2016.2631887","volume":"26","author":"Z Ding","year":"2017","unstructured":"Ding Z, Fu Y (2017) Robust transfer metric learning for image classification. IEEE Trans Image Process 26(2):660\u2013670","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"8143_CR11","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TNNLS.2016.2618765","volume":"29","author":"Z Ding","year":"2018","unstructured":"Ding Z, Shao M, Fu Y (2018) Incomplete multisource transfer learning. IEEE Trans Neural Netw Learn Syst 29(2):310\u2013323","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"8143_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8143_CR13","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Computer vision \u2013 ECCV 2016, pp 391\u2013407. Springer International Publishing","DOI":"10.1007\/978-3-319-46475-6_25"},{"issue":"7639","key":"8143_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa R A, Ko J, Swetter S M, Blau HMT (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (7639):115\u2013118","journal-title":"Nature"},{"key":"8143_CR15","doi-asserted-by":"crossref","unstructured":"Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: IEEE international conference on computer vision, pp 349\u2013356","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"8143_CR16","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics AISTATS, pp 249\u2013256"},{"issue":"4","key":"8143_CR17","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1137\/S0036144596301390","volume":"39","author":"D Gottlieb","year":"1997","unstructured":"Gottlieb D, Shu C W (1997) On the gibbs phenomenon and its resolution. Siam Rev 39(4):644\u2013668","journal-title":"Siam Rev"},{"issue":"1-2","key":"8143_CR18","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/0377-0427(92)90260-5","volume":"43","author":"D Gottlieb","year":"1992","unstructured":"Gottlieb D, Shu CW, Solomonoff A, Vandeven H (1992) On the gibbs phenomenon i: recovering exponential accuracy from the fourier partial sum of a nonperiodic analytic function. J Comput Appl Math 43(1-2):81\u201398","journal-title":"J Comput Appl Math"},{"issue":"22","key":"8143_CR19","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram MT (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402\u20132410","journal-title":"JAMA"},{"key":"8143_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8143_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision (ECCV), pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"8143_CR22","unstructured":"Hu Y, Gao X, Li J, Huang Y, Wang H (2018) Single image super-resolution via cascaded multi-scale cross network. arXiv:1802.08808 [cs.CV]"},{"key":"8143_CR23","unstructured":"Jain V, Seung H S (2008) Natural image denoising with convolutional networks. In: Proceedings of the 21st international conference on neural information processing systems, pp 769\u2013776"},{"issue":"5","key":"8143_CR24","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1002\/mrm.26054","volume":"76","author":"E Kellner","year":"2016","unstructured":"Kellner E, Dhital B, Kiselev VG, Reisert M (2016) Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76(5):1574\u20131581","journal-title":"Magn Reson Med"},{"key":"8143_CR25","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee K M (2016) Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"8143_CR26","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"8143_CR27","unstructured":"Kingma DP, Ba JL (2014) Adam: A method for stochastic optimization. arXiv:1412.6980v9 [cs.LG]"},{"key":"8143_CR28","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: International conference on neural information processing systems, pp 1097\u20131105"},{"key":"8143_CR29","unstructured":"Latt JL, Morrison A, Radgowski A, Tobin J, Viswanathan A (2016) Technical report: improved fourier reconstruction using jump information with applications to mri. arXiv:1610.03764v1 [cs.NA]"},{"issue":"7553","key":"8143_CR30","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"4","key":"8143_CR31","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y Lecun","year":"1989","unstructured":"Lecun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel L D (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","journal-title":"Neural Comput"},{"key":"8143_CR32","doi-asserted-by":"publisher","unstructured":"Li J, Lu K, Huang Z, Zhu L, Shen H T (2018) Heterogeneous domain adaptation through progressive alignment. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2018.2868854","DOI":"10.1109\/TNNLS.2018.2868854"},{"key":"8143_CR33","doi-asserted-by":"publisher","unstructured":"Li J, Lu K, Huang Z, Zhu L, Shen HT (2018) Transfer independently together: a generalized framework for domain adaptation. IEEE Transactions on Cybernetics. https:\/\/doi.org\/10.1109\/TCYB.2018.2820174","DOI":"10.1109\/TCYB.2018.2820174"},{"issue":"2","key":"8143_CR34","first-page":"67","volume":"4","author":"ZP Liang","year":"1992","unstructured":"Liang ZP, Boada FE, Constable RT, Haacke EM (1992) Constrained reconstruction methods in mr imaging. Reviews of Magnetic Resonance in Medicine 4 (2):67\u2013185","journal-title":"Reviews of Magnetic Resonance in Medicine"},{"key":"8143_CR35","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"9","key":"8143_CR36","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Aaa S, Ciompi F, Ghafoorian M (2017) A survey on deep learning in medical image analysis. Med Image Anal 42(9):60\u201388","journal-title":"Med Image Anal"},{"issue":"6","key":"8143_CR37","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1016\/j.media.2010.05.010","volume":"14","author":"JV Manjon","year":"2010","unstructured":"Manjon JV, Coupe P, Buades A, Fonov V, Collins D L, Robles M (2010) Non-local mri upsampling. Med Image Anal 14(6):784\u2013792","journal-title":"Med Image Anal"},{"key":"8143_CR38","unstructured":"Ouyang W, Luo P, Zeng X et al (2014) Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. arXiv:1409.3505"},{"issue":"10","key":"8143_CR39","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8143_CR40","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.neuroimage.2015.06.068","volume":"120","author":"D Perrone","year":"2015","unstructured":"Perrone D, Aelterman J, Pi\u017eurica A, Jeurissen B, Philips W, Leemans A (2015) The effect of gibbs ringing artifacts on measures derived from diffusion MRI. Neuroimage 120:441\u2013455","journal-title":"Neuroimage"},{"issue":"3","key":"8143_CR41","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"issue":"1","key":"8143_CR42","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s11075-005-9003-5","volume":"41","author":"SA Sarra","year":"2006","unstructured":"Sarra SA (2006) Digital total variation filtering as postprocessing for chebyshev pseudospectral methods for conservation laws. Numerical Algorithms 41(1):17\u201333","journal-title":"Numerical Algorithms"},{"issue":"1","key":"8143_CR43","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19(1):221\u2013248","journal-title":"Annu Rev Biomed Eng"},{"key":"8143_CR44","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"8143_CR45","unstructured":"Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988\u20131996"},{"key":"8143_CR46","unstructured":"Sun Y, Liang D, Wang X, Tang X (2015) DeepID3: face recognition with very deep neural networks. arXiv:1502.00873v1"},{"key":"8143_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8143_CR48","unstructured":"Szegedy C, Reed S, Erhan D, Anguelov D (2014) Scalable, highquality object detection. arXiv:1412.1441"},{"key":"8143_CR49","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2790\u20132798","DOI":"10.1109\/CVPR.2017.298"},{"key":"8143_CR50","unstructured":"Tong T, Li G, Liu X, Gao Q (2018) Image super-resolution using dense skip connections. In: 2017 IEEE international conference on computer vision (ICCV), pp 4809\u20134817"},{"issue":"1","key":"8143_CR51","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1002\/mrm.25866","volume":"76","author":"J Veraart","year":"2016","unstructured":"Veraart J, Fieremans E, Jelescu IO, Knoll F, Novikov DS (2016) Gibbs ringing in diffusion mri. Magn Reson Med 76(1):301\u2013314","journal-title":"Magn Reson Med"},{"issue":"4","key":"8143_CR52","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"8143_CR53","doi-asserted-by":"crossref","unstructured":"Wang Y, Song Y, Xie H, Li W, Hu B, Yang G (2017) Reduction of gibbs artifacts in magnetic resonance imaging based on convolutional neural network. In: International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1\u20135","DOI":"10.1109\/CISP-BMEI.2017.8302197"},{"issue":"99","key":"8143_CR54","first-page":"1","volume":"PP","author":"S Wang","year":"2018","unstructured":"Wang S, Ding Z, Fu Y (2018) Cross-generation kinship verification with sparse discriminative metric. IEEE Trans Pattern Analysis Mach Int PP(99):1\u20131","journal-title":"IEEE Trans Pattern Analysis Mach Int"},{"key":"8143_CR55","doi-asserted-by":"crossref","unstructured":"Wang T, Sun M, Hu K (2018) Dilated deep residual network for image denoising. arXiv:1708.05473 [cs.CV]","DOI":"10.1109\/ICTAI.2017.00192"},{"issue":"1","key":"8143_CR56","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/42.222669","volume":"12","author":"H Yan","year":"1993","unstructured":"Yan H, Mao J (1993) Data truncation artifact reduction in mr imaging using a multilayer neural network. IEEE Trans Med Imaging 12(1):73\u201377","journal-title":"IEEE Trans Med Imaging"},{"key":"8143_CR57","unstructured":"Zeiler MD, Fergus R (2013) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp 818\u2013833"},{"key":"8143_CR58","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. arXiv:1807.02758","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"8143_CR59","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. arXiv:1802.08797","DOI":"10.1109\/CVPR.2018.00262"},{"key":"8143_CR60","unstructured":"Zhao X, Zhang Y, Zhang T, Zou X (2018) Channel splitting network for single mr image super-resolution. arXiv:1810.06453"},{"issue":"11","key":"8143_CR61","doi-asserted-by":"publisher","first-page":"5264","DOI":"10.1109\/TNNLS.2018.2797248","volume":"29","author":"L Zhu","year":"2018","unstructured":"Zhu L, Huang Z, Li Z, Xie L, Shen HT (2018) Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE Trans Neural Netw Learn Syst 29(11):5264\u20135276","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"8143_CR62","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1109\/TMM.2017.2729025","volume":"19","author":"L Zhu","year":"2017","unstructured":"Zhu L, Huang Z, Liu X, He X, Sun J, Zhou X (2017) Discrete multi-modal hashing with canonical views for robust mobile landmark search. IEEE Trans Multimed 19(9):2066\u20132079","journal-title":"IEEE Trans Multimed"},{"issue":"2","key":"8143_CR63","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1109\/TKDE.2016.2562624","volume":"29","author":"L Zhu","year":"2017","unstructured":"Zhu L, Shen J, Xie L, Cheng Z (2017) Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans Knowl Data Eng 29 (2):472\u2013486","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08143-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-019-08143-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08143-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T13:54:12Z","timestamp":1722088452000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-019-08143-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,21]]},"references-count":63,"journal-issue":{"issue":"45-46","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["8143"],"URL":"https:\/\/doi.org\/10.1007\/s11042-019-08143-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,21]]},"assertion":[{"value":"3 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}