{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T01:15:28Z","timestamp":1783214128153,"version":"3.54.6"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114295","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:22:30Z","timestamp":1782145350000},"page":"114295","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["DWT-based Tensor Robust Principal Component Analysis for dynamic high-dimensional signals"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1598-2223","authenticated-orcid":false,"given":"Qile","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5074-1833","authenticated-orcid":false,"given":"Shun","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-7663","authenticated-orcid":false,"given":"Shiqian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoulie","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114295_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110591","article-title":"Matrix normal PCA for interpretable dimension reduction and graphical noise modeling","volume":"154","author":"Zhang","year":"2024","journal-title":"Pattern Recognit."},{"issue":"8","key":"10.1016\/j.patcog.2026.114295_b2","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1016\/j.patcog.2004.12.012","article-title":"A PCA-based watermarking scheme for tamper-proof of web pages","volume":"38","author":"Zhao","year":"2005","journal-title":"Pattern Recognit."},{"issue":"8","key":"10.1016\/j.patcog.2026.114295_b3","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1016\/j.patcog.2013.01.007","article-title":"Novel and efficient pedestrian detection using bidirectional PCA","volume":"46","author":"Kim","year":"2013","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114295_b4","article-title":"Time-series analysis of cellular shapes using transported velocity fields","author":"Deng","year":"2025","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.patcog.2026.114295_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis?","volume":"58","author":"Cand\u00e8s","year":"2011","journal-title":"J. ACM"},{"issue":"5","key":"10.1016\/j.patcog.2026.114295_b6","first-page":"5766","article-title":"Exact decomposition of joint low rankness and local smoothness plus sparse matrices","volume":"45","author":"Peng","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114295_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124885","article-title":"Audio\u2013Visual segmentation based on robust principal component analysis","volume":"256","author":"Fang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2026.114295_b8","doi-asserted-by":"crossref","unstructured":"S. Fang, Z. Xu, S. Wu, S. Xie, Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 1348\u20131357.","DOI":"10.1109\/CVPR52729.2023.00136"},{"issue":"2","key":"10.1016\/j.patcog.2026.114295_b9","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.patcog.2013.06.031","article-title":"Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion","volume":"47","author":"Luan","year":"2014","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114295_b10","doi-asserted-by":"crossref","unstructured":"C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, S. Yan, Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5249\u20135257.","DOI":"10.1109\/CVPR.2016.567"},{"issue":"10","key":"10.1016\/j.patcog.2026.114295_b11","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/TPAMI.2018.2881476","article-title":"Robust kronecker component analysis","volume":"41","author":"Bahri","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114295_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107252","article-title":"Smooth robust tensor principal component analysis for compressed sensing of dynamic MRI","volume":"102","author":"Liu","year":"2020","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.patcog.2026.114295_b13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2512329","article-title":"Most tensor problems are NP-hard","volume":"60","author":"Hillar","year":"2013","journal-title":"J. ACM"},{"key":"10.1016\/j.patcog.2026.114295_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110241","article-title":"Robust low tubal rank tensor recovery via L2E criterion","volume":"149","author":"Song","year":"2024","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.patcog.2026.114295_b15","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.laa.2010.09.020","article-title":"Factorization strategies for third-order tensors","volume":"435","author":"Kilmer","year":"2011","journal-title":"Linear Algebra Appl."},{"key":"10.1016\/j.patcog.2026.114295_b16","unstructured":"H. Qiu, Y. Wang, S. Tang, D. Meng, Q. Yao, Fast and Provable Nonconvex Tensor RPCA, in: International Conference on Machine Learning, 2022, pp. 18211\u201318249."},{"issue":"4","key":"10.1016\/j.patcog.2026.114295_b17","doi-asserted-by":"crossref","first-page":"1678","DOI":"10.1109\/TIP.2014.2305840","article-title":"Tensor-based formulation and nuclear norm regularization for multienergy computed tomography","volume":"23","author":"Semerci","year":"2014","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10.1016\/j.patcog.2026.114295_b18","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2019.2891760","article-title":"Tensor robust principal component analysis with a new tensor nuclear norm","volume":"42","author":"Lu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.patcog.2026.114295_b19","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/JSTSP.2018.2873142","article-title":"Improved robust tensor principal component analysis via low-rank core matrix","volume":"12","author":"Liu","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"10.1016\/j.patcog.2026.114295_b20","doi-asserted-by":"crossref","unstructured":"P. Zhou, J. Feng, Outlier-robust tensor PCA, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2263\u20132271.","DOI":"10.1109\/CVPR.2017.419"},{"issue":"6","key":"10.1016\/j.patcog.2026.114295_b21","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1109\/TPAMI.2020.3017672","article-title":"Enhanced tensor RPCA and its application","volume":"43","author":"Gao","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.patcog.2026.114295_b22","doi-asserted-by":"crossref","first-page":"10667","DOI":"10.1109\/TCYB.2021.3067676","article-title":"Robust tensor SVD and recovery with rank estimation","volume":"52","author":"Shi","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.patcog.2026.114295_b23","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.laa.2015.07.021","article-title":"Tensor\u2013tensor products with invertible linear transforms","volume":"485","author":"Kernfeld","year":"2015","journal-title":"Linear Algebra Appl."},{"issue":"28","key":"10.1016\/j.patcog.2026.114295_b24","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2015851118","article-title":"Tensor-tensor algebra for optimal representation and compression of multiway data","volume":"118","author":"Kilmer","year":"2021","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.patcog.2026.114295_b25","doi-asserted-by":"crossref","unstructured":"C. Lu, Transforms based Tensor Robust PCA: Corrupted Low-Rank Tensors Recovery via Convex Optimization, in: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 1125\u20131132.","DOI":"10.1109\/ICCV48922.2021.00118"},{"key":"10.1016\/j.patcog.2026.114295_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.107181","article-title":"Patched-tube unitary transform for robust tensor completion","volume":"100","author":"Ng","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114295_b27","article-title":"Slim is better: Transform-based tensor robust principal component analysis","author":"Chen","year":"2025","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.patcog.2026.114295_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111734","article-title":"Multidimensional nonlinear transform-based tensor representation for high-dimensional image reconstruction","author":"Shi","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114295_b29","article-title":"Time\u2013frequency signal processing: Today and future","volume":"110","author":"Akan","year":"2021","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.patcog.2026.114295_b30","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.3390\/app9071345","article-title":"Wavelet transform application for non-stationary time-series analysis","volume":"9","author":"Rhif","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.patcog.2026.114295_b31","doi-asserted-by":"crossref","first-page":"58869","DOI":"10.1109\/ACCESS.2022.3179517","article-title":"A review of wavelet analysis and its applications: Challenges and opportunities","volume":"10","author":"Guo","year":"2022","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.patcog.2026.114295_b32","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.patcog.2026.114295_b33","doi-asserted-by":"crossref","unstructured":"Z. Zhang, G. Ely, S. Aeron, N. Hao, M. Kilmer, Novel methods for multilinear data completion and de-noising based on tensor-SVD, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3842\u20133849.","DOI":"10.1109\/CVPR.2014.485"},{"key":"10.1016\/j.patcog.2026.114295_b34","first-page":"1007","article-title":"Two extended Bolzano\u2013Weierstrass theorems","author":"Kimber","year":"1965","journal-title":"Amer. Math. Monthly"},{"issue":"9","key":"10.1016\/j.patcog.2026.114295_b35","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/83.862633","article-title":"Adaptive wavelet thresholding for image denoising and compression","volume":"9","author":"Chang","year":"2000","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.patcog.2026.114295_b36","first-page":"33:1","article-title":"WTDUN: Wavelet tree-structured sampling and deep unfolding network for image compressed sensing","volume":"21","author":"Han","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.patcog.2026.114295_b37","doi-asserted-by":"crossref","unstructured":"S. Gu, L. Zhang, W. Zuo, X. Feng, Weighted nuclear norm minimization with application to image denoising, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2862\u20132869.","DOI":"10.1109\/CVPR.2014.366"},{"key":"10.1016\/j.patcog.2026.114295_b38","doi-asserted-by":"crossref","unstructured":"H. Dong, M. Shah, S. Donegan, Y. Chi, Deep unfolded tensor robust PCA with self-supervised learning, in: ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2023, pp. 1\u20135.","DOI":"10.1109\/ICASSP49357.2023.10095485"},{"key":"10.1016\/j.patcog.2026.114295_b39","first-page":"1","article-title":"MAC-Net: Model-aided nonlocal neural network for hyperspectral image denoising","volume":"60","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.patcog.2026.114295_b40","series-title":"The 2019 DAVIS challenge on VOS: Unsupervised multi-object segmentation","author":"Caelles","year":"2019"},{"issue":"6","key":"10.1016\/j.patcog.2026.114295_b41","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/TPAMI.2013.242","article-title":"Segmentation of moving objects by long term video analysis","volume":"36","author":"Ochs","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.114295_b42","doi-asserted-by":"crossref","unstructured":"F. Li, T. Kim, A. Humayun, D. Tsai, J.M. Rehg, Video segmentation by tracking many figure-ground segments, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2192\u20132199.","DOI":"10.1109\/ICCV.2013.273"},{"key":"10.1016\/j.patcog.2026.114295_b43","doi-asserted-by":"crossref","unstructured":"L. Maddalena, A. Petrosino, Towards benchmarking scene background initialization, in: International Conference on Image Analysis and Processing, 2015, pp. 469\u2013476.","DOI":"10.1007\/978-3-319-23222-5_57"},{"key":"10.1016\/j.patcog.2026.114295_b44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2016.12.024","article-title":"Scene background initialization: A taxonomy","volume":"96","author":"Bouwmans","year":"2017","journal-title":"Pattern Recogn. Lett."},{"issue":"11","key":"10.1016\/j.patcog.2026.114295_b45","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1109\/TIP.2017.2728181","article-title":"Extensive benchmark and survey of modeling methods for scene background initialization","volume":"26","author":"Jodoin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.patcog.2026.114295_b46","doi-asserted-by":"crossref","DOI":"10.54294\/g80ruo","article-title":"Evaluation framework for algorithms segmenting short axis cardiac MRI","author":"Radau","year":"2009","journal-title":"MIDAS J."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012604?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012604?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:20:46Z","timestamp":1783210846000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326012604"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":46,"alternative-id":["S0031320326012604"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114295","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DWT-based Tensor Robust Principal Component Analysis for dynamic high-dimensional signals","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114295","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114295"}}