{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:45:59Z","timestamp":1776116759966,"version":"3.50.1"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012449","name":"Southern University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012449","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M773057"],"award-info":[{"award-number":["2025M773057"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["ZDSYS20210623092007023"],"award-info":[{"award-number":["ZDSYS20210623092007023"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011523","name":"South China University of Technology Guangdong Key Laboratory of Innovation Methodology & Decision Management System","doi-asserted-by":"publisher","award":["2021222012"],"award-info":[{"award-number":["2021222012"]}],"id":[{"id":"10.13039\/501100011523","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.neucom.2026.133160","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:52:50Z","timestamp":1772038370000},"page":"133160","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["3DeepRep: 3D deep low-rank tensor representation for hyperspectral image inpainting"],"prefix":"10.1016","volume":"678","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9357-1416","authenticated-orcid":false,"given":"Yunshan","family":"Li","sequence":"first","affiliation":[]},{"given":"Wenwu","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Qianqian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lili","family":"Yang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133160_bib0005","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern trends in hyperspectral image analysis: a review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.133160_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112790","article-title":"Accurate hyperspectral imaging of mineralised outcrops: an example from lithium-bearing pegmatites at uis, Namibia","volume":"269","author":"Booysen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.neucom.2026.133160_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109037","article-title":"A systematic review of hyperspectral imaging in precision agriculture: analysis of its current state and future prospects","volume":"222","author":"Ram","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.neucom.2026.133160_bib0020","first-page":"1","article-title":"Dla-net: a dynamically learnable attention network for intelligent surface visual inspection of aero-engine blades","volume":"74","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.neucom.2026.133160_bib0025","article-title":"Ai-driven Automation of aviation equipment inspection: insights from a complex adaptive systems perspective","volume":"7","author":"Wu","year":"2026","journal-title":"The Innov."},{"key":"10.1016\/j.neucom.2026.133160_bib0030","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1109\/TGRS.2020.3024852","article-title":"Target detection through tree-structured encoding for hyperspectral images","volume":"59","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"10.1016\/j.neucom.2026.133160_bib0035","doi-asserted-by":"crossref","first-page":"6673","DOI":"10.1016\/j.asr.2025.09.028","article-title":"Hyperspectral image denoising through hybrid spectral transformer network","volume":"76","author":"Yadav","year":"2025","journal-title":"Adv. Space Res."},{"key":"10.1016\/j.neucom.2026.133160_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130428","article-title":"A comprehensive survey for hyperspectral image classification: the evolution from conventional to transformers and mamba models","author":"Ahmad","year":"2025","journal-title":"Neurocomputing"},{"issue":"12","key":"10.1016\/j.neucom.2026.133160_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e33208","article-title":"Hyperspectral imaging and its applications: a review","volume":"10","author":"Bhargava","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.neucom.2026.133160_bib0050","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"10.1016\/j.neucom.2026.133160_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130998","article-title":"Lightweight memory-driven self-attention for hyperspectral image classification with cnn-transformer cross-feature fusion","author":"Liang","year":"2025","journal-title":"Neurocomputing"},{"issue":"8","key":"10.1016\/j.neucom.2026.133160_bib0060","doi-asserted-by":"crossref","first-page":"10736","DOI":"10.1109\/TNNLS.2023.3243808","article-title":"Hyperspectral tensor completion using low-rank modeling and convex functional analysis","volume":"35","author":"Lin","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"10.1016\/j.neucom.2026.133160_bib0065","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.apm.2023.06.031","article-title":"Accurate regularized tucker decomposition for image restoration","volume":"123","author":"Gong","year":"2023","journal-title":"Appl. Math. Model."},{"issue":"6","key":"10.1016\/j.neucom.2026.133160_bib0070","first-page":"421","article-title":"Image inpainting via smooth tucker decomposition and low-rank hankel constraint","volume":"45","author":"Cai","year":"2023","journal-title":"Int. J. Comput. Appl."},{"key":"10.1016\/j.neucom.2026.133160_bib0075","author":"Zhao"},{"key":"10.1016\/j.neucom.2026.133160_bib0080","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Fully-connected tensor network decomposition and its application to higher-order tensor completion","author":"Zheng","year":"2021"},{"issue":"1","key":"10.1016\/j.neucom.2026.133160_bib0085","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s10915-022-01841-8","article-title":"Tensor completion via fully-connected tensor network decomposition with regularized factors","volume":"92","author":"Zheng","year":"2022","journal-title":"J. Sci. Comput."},{"issue":"1","key":"10.1016\/j.neucom.2026.133160_bib0090","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3934\/ipi.2024025","article-title":"Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal","volume":"19","author":"Tu","year":"2025","journal-title":"Inverse Probl. Imaging"},{"key":"10.1016\/j.neucom.2026.133160_bib0095","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1109\/TIT.2019.2959980","article-title":"Low-tubal-rank tensor completion using alternating minimization","volume":"66","author":"Liu","year":"2016","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10.1016\/j.neucom.2026.133160_bib0100","doi-asserted-by":"crossref","first-page":"5135","DOI":"10.1109\/TIP.2019.2916734","article-title":"Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization","volume":"28","author":"Dian","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neucom.2026.133160_bib0105","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TIP.2020.3031813","article-title":"Low-rank preserving t-linear projection for robust image feature extraction","volume":"30","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neucom.2026.133160_bib0110","series-title":"2014 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3842","article-title":"Novel methods for multilinear data completion and de-noising based on tensor-SVD","author":"Zhang","year":"2014"},{"key":"10.1016\/j.neucom.2026.133160_bib0115","author":"Xu"},{"key":"10.1016\/j.neucom.2026.133160_bib0120","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/TIP.2020.3000349","article-title":"Framelet representation of tensor nuclear norm for third-order tensor completion","volume":"29","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.neucom.2026.133160_bib0125","doi-asserted-by":"crossref","DOI":"10.1002\/nla.2299","article-title":"Robust tensor completion using transformed tensor singular value decomposition","volume":"27","author":"Song","year":"2020","journal-title":"Numer. Linear Algebra Appl."},{"key":"10.1016\/j.neucom.2026.133160_bib0130","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1007\/s10994-021-05987-8","article-title":"Tensor q-rank: new data dependent definition of tensor rank","volume":"110","author":"Kong","year":"2019","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2026.133160_bib0135","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1109\/TNNLS.2021.3104837","article-title":"Dictionary learning with Low-Rank coding coefficients for tensor completion","volume":"34","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"3","key":"10.1016\/j.neucom.2026.133160_bib0140","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10915-022-01937-1","article-title":"Nonlinear transform induced tensor nuclear norm for tensor completion","volume":"92","author":"Li","year":"2022","journal-title":"J. Sci. Comput."},{"key":"10.1016\/j.neucom.2026.133160_bib0145","doi-asserted-by":"crossref","first-page":"3793","DOI":"10.1109\/TIP.2022.3176220","article-title":"Self-supervised nonlinear transform-based tensor nuclear norm for multi-dimensional image recovery","volume":"31","author":"Luo","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"10.1016\/j.neucom.2026.133160_bib0150","doi-asserted-by":"crossref","first-page":"8969","DOI":"10.1109\/TNNLS.2022.3217198","article-title":"CoNoT: coupled nonlinear transform-based low-rank tensor representation for multidimensional image completion","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.133160_bib0155","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ins.2020.05.005","article-title":"Tensor n-tubal rank and its convex relaxation for low-rank tensor recovery","volume":"532","author":"Zheng","year":"2018","journal-title":"Inf. Sci."},{"issue":"3","key":"10.1016\/j.neucom.2026.133160_bib0160","doi-asserted-by":"crossref","first-page":"727","DOI":"10.4208\/nmtma.OA-2023-0158","article-title":"Multi-dimensional image recovery via self-supervised nonlinear transform based a three-directional tensor nuclear norm","volume":"17","author":"Li","year":"2024","journal-title":"Numer. Math.-Theory Methods Appl."},{"key":"10.1016\/j.neucom.2026.133160_bib0165","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1364\/AO.420305","article-title":"Compressive spectral image reconstruction using deep prior and low-rank tensor representation","volume":"60 14","author":"Bacca","year":"2021","journal-title":"Appl. Opt."},{"key":"10.1016\/j.neucom.2026.133160_bib0170","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"19281","article-title":"HLRTF: hierarchical low-rank tensor factorization for inverse problems in multi-dimensional imaging","author":"Luo","year":"2022"},{"key":"10.1016\/j.neucom.2026.133160_bib0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2025.109903","article-title":"Deep fully-connected tensor network decomposition for multi-dimensional signal recovery","volume":"233","author":"Su","year":"2025","journal-title":"Signal Process."},{"issue":"1","key":"10.1016\/j.neucom.2026.133160_bib0180","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TGRS.2019.2940534","article-title":"Mixed noise removal in hyperspectral image via low-fibered-rank regularization","volume":"58","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.133160_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2024.109407","article-title":"Tensor completion via joint reweighted tensor q-nuclear norm for visual data recovery","volume":"219","author":"Cheng","year":"2024","journal-title":"Signal Process."},{"key":"10.1016\/j.neucom.2026.133160_bib0190","first-page":"1","article-title":"DTR: a unified deep tensor representation framework for multimedia data recovery","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Multimedia"},{"key":"10.1016\/j.neucom.2026.133160_bib0195","series-title":"Proc. Icml","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"vol. 30","author":"Maas","year":"2013"},{"key":"10.1016\/j.neucom.2026.133160_bib0200","doi-asserted-by":"crossref","first-page":"10990","DOI":"10.1109\/TPAMI.2023.3259640","article-title":"Guaranteed tensor recovery fused low-rankness and smoothness","volume":"45","author":"Wang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10.1016\/j.neucom.2026.133160_bib0205","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. Optim."},{"key":"10.1016\/j.neucom.2026.133160_bib0210","author":"Adam"},{"key":"10.1016\/j.neucom.2026.133160_bib0215","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TSP.2016.2639466","article-title":"Exact tensor completion using t-SVD","volume":"65","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.neucom.2026.133160_bib0220","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3005","article-title":"Hir-diff: unsupervised hyperspectral image restoration via improved diffusion models","author":"Pang","year":"2024"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226005576?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226005576?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:51:15Z","timestamp":1776113475000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226005576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":44,"alternative-id":["S0925231226005576"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133160","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"3DeepRep: 3D deep low-rank tensor representation for hyperspectral image inpainting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133160","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133160"}}