{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:12:52Z","timestamp":1767139972197,"version":"build-2238731810"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T00:00:00Z","timestamp":1611360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T00:00:00Z","timestamp":1611360000000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00530-021-00750-4","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T23:34:21Z","timestamp":1611358461000},"page":"503-518","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep reconstruction of 1D ISOMAP representations"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6294-1581","authenticated-orcid":false,"given":"Honggui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitri","family":"Galayko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,23]]},"reference":[{"issue":"1","key":"750_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/TNNLS.2018.2836802","volume":"30","author":"W Xian","year":"2018","unstructured":"Xian, W., Hao, S., Yuanxiang, Li.: Reconstructible nonlinear dimensionality reduction via joint dictionary learning. IEEE Trans. Neural Netw. Learn. Syst. 30(1), 175\u2013189 (2018). https:\/\/doi.org\/10.1109\/TNNLS.2018.2836802","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5500","key":"750_CR2","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000). https:\/\/doi.org\/10.1126\/science.290.5500.2319","journal-title":"Science"},{"issue":"7697","key":"750_CR3","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J.Z., Cauley, S.F.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487 (2018)","journal-title":"Nature"},{"issue":"7","key":"750_CR4","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. 26(7), 3142\u20133155 (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2662206","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"750_CR5","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/MSP.2017.2717489","volume":"34","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Zuo, W.: Image restoration: from sparse and low-rank priors to deep priors. IEEE Signal Process. Mag. 34(5), 172\u2013179 (2017). https:\/\/doi.org\/10.1109\/MSP.2017.2717489","journal-title":"IEEE Signal Process. Mag."},{"issue":"3","key":"750_CR6","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s00530-017-0551-z","volume":"24","author":"H Li","year":"2018","unstructured":"Li, H.: 1D representation of Isomap for united video coding. Multimedia Syst. 24(3), 297\u2013312 (2018). https:\/\/doi.org\/10.1007\/s00530-017-0551-z","journal-title":"Multimedia Syst."},{"issue":"9","key":"750_CR7","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"Z Kai","year":"2018","unstructured":"Kai, Z., Wangmeng, Z., Lei, Z.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608\u20134622 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2839891","journal-title":"IEEE Trans. Image Process."},{"key":"750_CR8","doi-asserted-by":"publisher","unstructured":"Li, J., Yu, J., Xu, L.: A cascaded algorithm for image quality assessment and image denoising based on CNN for image security and authorization. Secur. Commun. Netw. Article Number: UNSP 8176984 (2018) https:\/\/doi.org\/10.1155\/2018\/8176984.","DOI":"10.1155\/2018\/8176984"},{"issue":"9","key":"750_CR9","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1109\/LSP.2018.2856619","volume":"25","author":"Y Yuan","year":"2018","unstructured":"Yuan, Y., Cao, Z., Su, L.: Light-field image superresolution using a combined deep CNN based on EPI. IEEE Signal Process. Lett. 25(9), 1359\u20131363 (2018). https:\/\/doi.org\/10.1109\/LSP.2018.2856619","journal-title":"IEEE Signal Process. Lett."},{"issue":"7","key":"750_CR10","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1109\/LSP.2018.2829766","volume":"25","author":"C Ren","year":"2018","unstructured":"Ren, C., He, X., Pu, Y.: Nonlocal similarity modeling and deep CNN gradient prior for super resolution. IEEE Signal Process. Lett. 25(7), 916\u2013920 (2018). https:\/\/doi.org\/10.1109\/LSP.2018.2829766","journal-title":"IEEE Signal Process. Lett."},{"issue":"6","key":"750_CR11","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","volume":"27","author":"M Zhang","year":"2018","unstructured":"Zhang, M., Li, W., Du, Q.: Diverse region-based CNN for hyperspectral image classification. IEEE Trans. Image Process. 27(6), 2623\u20132634 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2809606","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"750_CR12","doi-asserted-by":"publisher","first-page":"10251","DOI":"10.1007\/s11042-017-5443-x","volume":"77","author":"Y Guo","year":"2018","unstructured":"Guo, Y., Liu, Y., Bakker, E.M.: EM CNN-RNN: a large-scale hierarchical image classification framework. Multimed. Tools Appl. 77(8), 10251\u201310271 (2018). https:\/\/doi.org\/10.1007\/s11042-017-5443-x","journal-title":"Multimed. Tools Appl."},{"key":"750_CR13","doi-asserted-by":"publisher","unstructured":"Li, J., Qiu, T., Wen, C.: Robust face recognition using the deep C2D-CNN model based on decision-level fusion, Sensors 18(7), Article Number: 2080 (2018) https:\/\/doi.org\/10.3390\/s18072080","DOI":"10.3390\/s18072080"},{"issue":"7","key":"750_CR14","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1109\/TPAMI.2018.2842770","volume":"41","author":"R He","year":"2019","unstructured":"He, R., Wu, X., Sun, Z.: Wasserstein CNN: learning invariant features for NIR-VIS face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1761\u20131773 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2842770","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"750_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2975798","author":"Y Chenggang","year":"2020","unstructured":"Chenggang, Y., Biao, G., Yuxuan, W., Yue, G.: Deep Multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https:\/\/doi.org\/10.1109\/TPAMI.2020.2975798","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"750_CR16","doi-asserted-by":"publisher","first-page":"3104","DOI":"10.1109\/TMM.2020.2967645","volume":"22","author":"C Yan","year":"2020","unstructured":"Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Feng, Xu.: 3D room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3104\u20133124 (2020). https:\/\/doi.org\/10.1109\/TMM.2020.2967645","journal-title":"IEEE Trans. Multimed."},{"key":"750_CR17","doi-asserted-by":"crossref","unstructured":"Chenggang, Y., Zhisheng, L., Yongbing, Z., Yutao, L., Xiangyang J., Yongdong Z.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. pp. 1\u201317. Preprint at  arXiv:2008.03741v1[cs.CV] (2020)","DOI":"10.1145\/3404374"},{"key":"750_CR18","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.neucom.2018.05.115","volume":"312","author":"X Weiying","year":"2018","unstructured":"Weiying, X., Yunsong, Li., Xiuping, J.: Deep convolutional networks with residual learning for accurate spectral-spatial denoising. Neurocomputing 312, 372\u2013381 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2018.05.115","journal-title":"Neurocomputing"},{"key":"750_CR19","doi-asserted-by":"publisher","unstructured":"Chen, C., Xu, Z.: Aerial-image denoising based on convolutional neural network with multi-scale residual learning approach. Information. 9(7), Article Number: UNSP 169 (2018). https:\/\/doi.org\/10.3390\/info9070169.","DOI":"10.3390\/info9070169"},{"issue":"6","key":"750_CR20","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.3390\/info9070169","volume":"45","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., MacDougall, R., Yu, H.: Residual learning based projection domain denoising for low-dose CT. Med. Phys. 45(6), E215\u2013E216 (2018). https:\/\/doi.org\/10.3390\/info9070169","journal-title":"Med. Phys."},{"key":"750_CR21","doi-asserted-by":"publisher","unstructured":"Zhan, Q., Yuan, Q., Li, J.: Learning a dilated residual network for SAR image despeckling. Remote Sens. 10(2), Article Number: 196 (2018). https:\/\/doi.org\/10.3390\/rs10020196","DOI":"10.3390\/rs10020196"},{"key":"750_CR22","doi-asserted-by":"publisher","unstructured":"Shi, J., Liu, Q., Wang, C.: Super-resolution reconstruction of MR image with a novel residual learning network algorithm. Phys. Med. Biol. 63(8), Article Number: 085011 (2018). https:\/\/doi.org\/10.1088\/1361-6560\/aab9e9","DOI":"10.1088\/1361-6560\/aab9e9"},{"key":"750_CR23","doi-asserted-by":"publisher","first-page":"23767","DOI":"10.1109\/ACCESS.2018.2829908","volume":"6","author":"W Wenjun","year":"2018","unstructured":"Wenjun, W., Chao, R., Xiaohai, He.: Video super-resolution via residual learning. IEEE Access 6, 23767\u201323777 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2829908","journal-title":"IEEE Access"},{"issue":"4","key":"750_CR24","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/TCI.2017.2671360","volume":"3","author":"L Dingyi","year":"2017","unstructured":"Dingyi, L., Zengfu, W.: Video superresolution via motion compensation and deep residual learning. IEEE Trans. Comput. Imaging 3(4), 749\u2013762 (2017). https:\/\/doi.org\/10.1109\/TCI.2017.2671360","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"12","key":"750_CR25","doi-asserted-by":"publisher","first-page":"5895","DOI":"10.1109\/TIP.2017.2750403","volume":"26","author":"Y Wenhan","year":"2017","unstructured":"Wenhan, Y., Jiashi, F., Jianchao, Y.: Deep edge guided recurrent residual learning for image super-resolution. IEEE Trans. Image Process. 26(12), 5895\u20135907 (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2750403","journal-title":"IEEE Trans. Image Process."},{"key":"750_CR26","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.cmpb.2018.05.024","volume":"162","author":"Q Feiwei","year":"2018","unstructured":"Feiwei, Q., Nannan, G., Yong, P.: Fine-grained leukocyte classification with deep residual learning for microscopic images. Comput. Methods Programs Biomed. 162, 243\u2013252 (2018). https:\/\/doi.org\/10.1016\/j.cmpb.2018.05.024","journal-title":"Comput. Methods Programs Biomed."},{"key":"750_CR27","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.patcog.2018.02.006","volume":"79","author":"L Haijun","year":"2018","unstructured":"Haijun, L., Tao, H., Feng, Z.: A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning. Pattern Recogn. 79, 290\u2013302 (2018). https:\/\/doi.org\/10.1016\/j.patcog.2018.02.006","journal-title":"Pattern Recogn."},{"key":"750_CR28","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.compbiomed.2018.02.008","volume":"95","author":"P McAllister","year":"2018","unstructured":"McAllister, P., Zheng, H., Bond, R.: Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets. Comput. Biol. Med. 95, 217\u2013233 (2018). https:\/\/doi.org\/10.1016\/j.compbiomed.2018.02.008","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"750_CR29","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","volume":"56","author":"Z Zilong","year":"2018","unstructured":"Zilong, Z., Jonathan, Li., Zhiming, L.: Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847\u2013858 (2018). https:\/\/doi.org\/10.1109\/TGRS.2017.2755542","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"750_CR30","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","volume":"56","author":"L Mou","year":"2018","unstructured":"Mou, L., Ghamisi, P., Zhu, X.X.: Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sending 56(1), 391\u2013406 (2018). https:\/\/doi.org\/10.1109\/TGRS.2017.2748160","journal-title":"IEEE Trans. Geosci. Remote Sending"},{"key":"750_CR31","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.compag.2017.08.005","volume":"141","author":"C Xi","year":"2017","unstructured":"Xi, C., Youhua, Z., Yiqiong, C.: Pest identification via deep residual learning in complex background. Comput. Electron. Agric. 141, 351\u2013356 (2017). https:\/\/doi.org\/10.1016\/j.compag.2017.08.005","journal-title":"Comput. Electron. Agric."},{"issue":"10","key":"750_CR32","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1109\/LGRS.2017.2736020","volume":"14","author":"W Yancong","year":"2017","unstructured":"Yancong, W., Qiangqiang, Y., Huanfeng, S.: Boosting the accuracy of multispectral image pansharpening by learning a deep residual network. IEEE Geosci. Remote Sens. Lett. 14(10), 1795\u20131799 (2017). https:\/\/doi.org\/10.1109\/LGRS.2017.2736020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"9","key":"750_CR33","doi-asserted-by":"publisher","first-page":"10437","DOI":"10.1007\/s11042-017-4440-4","volume":"77","author":"W Songtao","year":"2018","unstructured":"Songtao, W., Shenghua, Z., Yan, L.: Deep residual learning for image steganalysis. Multimed. Tools Appl. 77(9), 10437\u201310453 (2018). https:\/\/doi.org\/10.1007\/s11042-017-4440-4","journal-title":"Multimed. Tools Appl."},{"issue":"7","key":"750_CR34","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1364\/BOE.9.003244","volume":"9","author":"SK Devalla","year":"2018","unstructured":"Devalla, S.K., Renukanand, P.K., Sreedhar, B.K.: DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed. Opt. Express 9(7), 3244\u20133265 (2018). https:\/\/doi.org\/10.1364\/BOE.9.003244","journal-title":"Biomed. Opt. Express"},{"issue":"8","key":"750_CR35","doi-asserted-by":"publisher","first-page":"2196","DOI":"10.1109\/TMM.2017.2780762","volume":"20","author":"F Yang-Yu","year":"2018","unstructured":"Yang-Yu, F., Shu, L., Bo, Li.: Label distribution-based facial attractiveness computation by deep residual learning. IEEE Trans. Multimed. 20(8), 2196\u20132208 (2018). https:\/\/doi.org\/10.1109\/TMM.2017.2780762","journal-title":"IEEE Trans. Multimed."},{"issue":"12","key":"750_CR36","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1109\/TCSVT.2012.2221191","volume":"22","author":"GJ Sullivan","year":"2012","unstructured":"Sullivan, G.J., Ohm, J.-R., Han, W.-J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649\u20131668 (2012). https:\/\/doi.org\/10.1109\/TCSVT.2012.2221191","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"750_CR37","first-page":"2579","volume":"9","author":"LJP van der Maaten","year":"2008","unstructured":"van der Maaten, L.J.P., Hinton, G.E.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9(2), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"updated-by":[{"DOI":"10.1007\/s00530-021-00821-6","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000}}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00750-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-021-00750-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00750-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T22:10:47Z","timestamp":1623967847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-021-00750-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,23]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["750"],"URL":"https:\/\/doi.org\/10.1007\/s00530-021-00750-4","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,23]]},"assertion":[{"value":"26 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2021","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00530-021-00821-6","URL":"https:\/\/doi.org\/10.1007\/s00530-021-00821-6","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}