{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:41:22Z","timestamp":1783536082925,"version":"3.55.0"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational and Applied Mathematics"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.cam.2026.117928","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T15:16:15Z","timestamp":1783350975000},"page":"117928","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A full-mode transformed tensor multi-rank method for mixed noise removal in 3D images"],"prefix":"10.1016","volume":"490","author":[{"given":"Yao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6809-7097","authenticated-orcid":false,"given":"Hongwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cam.2026.117928_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.04.010","article-title":"Low-rank with sparsity constraints for image denoising","volume":"637","author":"Ou","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.cam.2026.117928_bib0002","doi-asserted-by":"crossref","first-page":"2433","DOI":"10.1109\/TIP.2022.3155949","article-title":"Low-rank high-order tensor completion with applications in visual data","volume":"31","author":"Qin","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.cam.2026.117928_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122323","article-title":"Kolmogorov-Arnold network-based enhanced fusion transformer for hyperspectral image classification","volume":"717","author":"Han","year":"2025","journal-title":"Inf. Sci."},{"key":"10.1016\/j.cam.2026.117928_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123720","article-title":"3D attention-focused pure convolutional target detection algorithm for insulator defect detection","volume":"249","author":"Lu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.cam.2026.117928_bib0005","first-page":"1","article-title":"Hyperspectral anomaly detection fused unified nonconvex tensor ring factors regularization","volume":"63","author":"Qin","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.cam.2026.117928_bib0006","first-page":"1","article-title":"Tensor ring decomposition-based generalized and efficient nonconvex approach for hyperspectral anomaly detection","volume":"62","author":"Qin","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.cam.2026.117928_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2024.114514","article-title":"Unsupervised object-based spectral unmixing for subpixel mapping","volume":"318","author":"Zhang","year":"2025","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.cam.2026.117928_bib0008","article-title":"Deep learning for medical image segmentation: state-of-the-art advancements and challenges","volume":"47","author":"Rayed","year":"2024","journal-title":"Inform. Med. Unlocked"},{"issue":"7","key":"10.1016\/j.cam.2026.117928_bib0009","first-page":"7704","article-title":"Efficient lightweight image denoising with triple attention transformer","volume":"38","author":"Zhou","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.cam.2026.117928_bib0010","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.apm.2023.10.023","article-title":"Wavelet analysis model inspired convolutional neural networks for image denoising","volume":"125","author":"Xu","year":"2024","journal-title":"Appl. Math. Model."},{"issue":"1","key":"10.1016\/j.cam.2026.117928_bib0011","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/TNNLS.2020.2978756","article-title":"3-D quasi-recurrent neural network for hyperspectral image denoising","volume":"32","author":"Wei","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.cam.2026.117928_bib0012","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2043","article-title":"Recorrupted-to-Recorrupted: unsupervised deep learning for image denoising","author":"Pang","year":"2021"},{"key":"10.1016\/j.cam.2026.117928_bib0013","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.neucom.2022.01.057","article-title":"Deep plug-and-play prior for hyperspectral image restoration","volume":"481","author":"Lai","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.cam.2026.117928_bib0014","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/TBDATA.2023.3254156","article-title":"Robust low transformed multi-rank tensor completion with deep prior regularization for multi-dimensional image recovery","volume":"9","author":"Li","year":"2023","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.cam.2026.117928_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110735","article-title":"Poisson tensor completion with transformed correlated total variation regularization","volume":"156","author":"Feng","year":"2024","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.cam.2026.117928_bib0016","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10915-024-02509-1","article-title":"Enhanced low-rank tensor recovery fusing reweighted tensor correlated total variation regularization for image denoising","volume":"99","author":"Huang","year":"2024","journal-title":"J. Sci. Comput."},{"key":"10.1016\/j.cam.2026.117928_bib0017","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"7994","article-title":"Tensor compressive sensing fused low-rankness and local smoothness","volume":"vol. 37","author":"Liu","year":"2023"},{"issue":"9","key":"10.1016\/j.cam.2026.117928_bib0018","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":"3","key":"10.1016\/j.cam.2026.117928_bib0019","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/TCYB.2017.2677944","article-title":"Denoising hyperspectral image with non-i.i.d. noise structure","volume":"48","author":"Chen","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.cam.2026.117928_bib0020","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6868","article-title":"Non-local meets global: an integrated paradigm for hyperspectral denoising","author":"He","year":"2019"},{"issue":"10","key":"10.1016\/j.cam.2026.117928_bib0021","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1109\/TPAMI.2020.2986773","article-title":"Low-tubal-rank plus sparse tensor recovery with prior subspace information","volume":"43","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cam.2026.117928_bib0022","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1109\/TIP.2026.3659302","article-title":"Double nonconvex tensor robust kernel principal component analysis and its visual applications","volume":"35","author":"Wu","year":"2026","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.cam.2026.117928_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109545","article-title":"Low-tubal-rank tensor recovery with multilayer subspace prior learning","volume":"140","author":"Kong","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.cam.2026.117928_bib0024","series-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","first-page":"2649","article-title":"Robust low-tubal-rank tensor completion via convex optimization","author":"Jiang","year":"2019"},{"issue":"8","key":"10.1016\/j.cam.2026.117928_bib0025","first-page":"4355","article-title":"Robust low-tubal-rank tensor recovery from binary measurements","volume":"44","author":"Hou","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cam.2026.117928_bib0026","article-title":"Generalized subspace coupling approach for robust low-tubal-rank tensor completion","author":"Kong","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.cam.2026.117928_bib0027","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1109\/TIP.2024.3385284","article-title":"Nonconvex robust high-order tensor completion using randomized low-rank approximation","volume":"33","author":"Qin","year":"2024","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.cam.2026.117928_bib0028","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.cam.2026.117928_bib0029","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.cam.2026.117928_bib0030","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.apm.2017.04.002","article-title":"A non-convex tensor rank approximation for tensor completion","volume":"48","author":"Ji","year":"2017","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.cam.2026.117928_bib0031","article-title":"Hyperspectral image denoising using non-convex fraction function","volume":"48","author":"Liu","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"10.1016\/j.cam.2026.117928_bib0032","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":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"10.1016\/j.cam.2026.117928_bib0033","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1109\/TPAMI.2018.2858249","article-title":"Large-scale low-rank matrix learning with nonconvex regularizers","volume":"41","author":"Yao","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cam.2026.117928_bib0034","unstructured":"Z. Lin, M. Chen, Y. Ma, The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices, (2010). arXiv preprint arXiv: 1009.5055."},{"issue":"4","key":"10.1016\/j.cam.2026.117928_bib0035","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10.1016\/j.cam.2026.117928_bib0036","first-page":"4239","article-title":"Low rank tensor completion with poisson observations","volume":"44","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Journal of Computational and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377042726005704?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0377042726005704?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:14:22Z","timestamp":1783530862000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0377042726005704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":36,"alternative-id":["S0377042726005704"],"URL":"https:\/\/doi.org\/10.1016\/j.cam.2026.117928","relation":{},"ISSN":["0377-0427"],"issn-type":[{"value":"0377-0427","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A full-mode transformed tensor multi-rank method for mixed noise removal in 3D images","name":"articletitle","label":"Article Title"},{"value":"Journal of Computational and Applied Mathematics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cam.2026.117928","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":"117928"}}