{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T11:40:41Z","timestamp":1783338041273,"version":"3.54.6"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2025YFA1016400"],"award-info":[{"award-number":["2025YFA1016400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12371517"],"award-info":[{"award-number":["12371517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024NSFSC0038"],"award-info":[{"award-number":["2024NSFSC0038"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.sigpro.2026.110714","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T02:24:40Z","timestamp":1779675880000},"page":"110714","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["LBNet: Linearized Bregman algorithm-based deep unfolding network for hyperspectral image unmixing"],"prefix":"10.1016","volume":"248","author":[{"given":"Guo-Liang","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7684-2533","authenticated-orcid":false,"given":"Jie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0256-1032","authenticated-orcid":false,"given":"Hong-Ji","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.sigpro.2026.110714_bib0001","first-page":"145","article-title":"A review of hyperspectral remote sensing and its application in vegetation and water resource studies","volume":"33","author":"Govender","year":"2007","journal-title":"Water S.A."},{"issue":"12","key":"10.1016\/j.sigpro.2026.110714_bib0002","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.tifs.2007.06.001","article-title":"Hyperspectral imaging\u2013an emerging process analytical tool for food quality and safety control","volume":"18","author":"Gowen","year":"2007","journal-title":"Trends Food Sci. Technol."},{"key":"10.1016\/j.sigpro.2026.110714_bib0003","series-title":"Geo-Spat. and Temp. Image and Data Exploit. III","first-page":"215","article-title":"Hyperspectral imaging applied to medical diagnoses and food safety","volume":"5097","author":"Carrasco","year":"2003"},{"issue":"11","key":"10.1016\/j.sigpro.2026.110714_bib0004","doi-asserted-by":"crossref","first-page":"11709","DOI":"10.1109\/TCYB.2021.3070577","article-title":"A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification","volume":"52","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"10.1016\/j.sigpro.2026.110714_bib0005","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.1109\/TCYB.2022.3233108","article-title":"Spatial invariant tensor self-Representation model for hyperspectral anomaly detection","volume":"54","author":"Sun","year":"2024","journal-title":"IEEE Trans. Cybern."},{"issue":"10","key":"10.1016\/j.sigpro.2026.110714_bib0006","doi-asserted-by":"crossref","first-page":"4469","DOI":"10.1109\/TCYB.2019.2951572","article-title":"Nonlocal sparse tensor factorization for semiblind hyperspectral and multispectral image fusion","volume":"50","author":"Dian","year":"2020","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"10.1016\/j.sigpro.2026.110714_bib0007","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1109\/TGRS.2007.898443","article-title":"Spectral and spatial complexity-Based hyperspectral unmixing","volume":"45","author":"Jia","year":"2007","journal-title":"IEEE Tran. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0008","series-title":"Proc. IEEE Int. Geosci. and Remote Sens. Symp. (IGARSS)","first-page":"2189","article-title":"Deep learning in hyperspectral unmixing: a review","author":"Bhatt","year":"2020"},{"issue":"2","key":"10.1016\/j.sigpro.2026.110714_bib0009","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Bioucas-Dias","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"4","key":"10.1016\/j.sigpro.2026.110714_bib0010","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: a fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0011","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0012","series-title":"Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)","first-page":"21","article-title":"Sparse unmixing of hyperspectral data: the legacy of SUnSAL","author":"Parente","year":"2021"},{"issue":"5","key":"10.1016\/j.sigpro.2026.110714_bib0013","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1109\/TGRS.2012.2213825","article-title":"Manifold regularized sparse NMF for hyperspectral unmixing","volume":"51","author":"Lu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0014","first-page":"1","article-title":"Model-based deep autoencoder networks for nonlinear hyperspectral unmixing","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.sigpro.2026.110714_bib0015","series-title":"Proc. Int. Conf. on Image Process. Theory, Tools and Appl. (IPTA)","first-page":"1","article-title":"Hyperspectral image analysis using deep learning \u2013 A review","author":"Petersson","year":"2016"},{"key":"10.1016\/j.sigpro.2026.110714_bib0016","series-title":"Proc. 7th Workshop Hyperspectral Image Signal Process. Evol. Remote Sens. (WHISPERS)","first-page":"1","article-title":"Hyperspectral image unmixing using autoencoder cascade","author":"Guo","year":"2015"},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0017","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/TGRS.2018.2868690","article-title":"UDAS: an untied denoising autoencoder with sparsity for spectral unmixing","volume":"57","author":"Qu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0018","first-page":"1","article-title":"UnDIP: hyperspectral unmixing using deep image prior","volume":"60","author":"Rasti","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0019","first-page":"1","article-title":"MisiCNet: minimum simplex convolutional network for deep hyperspectral unmixing","volume":"60","author":"Rasti","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0020","first-page":"1","article-title":"DAAN: A deep autoencoder-based augmented network for blind multilinear hyperspectral unmixing","volume":"62","author":"Su","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0021","first-page":"1","article-title":"EMLM-Net: an extended multilinear mixing model-inspired dual-stream network for unsupervised nonlinear hyperspectral unmixing","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0022","doi-asserted-by":"crossref","first-page":"5704","DOI":"10.1109\/JSTARS.2022.3189551","article-title":"NMF-DuNet: nonnegative matrix factorization inspired deep unrolling networks for hyperspectral and multispectral image fusion","volume":"15","author":"Khader","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0023","series-title":"Proc. Adv. Neural Inf. Process. Syst.","first-page":"9061","article-title":"Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds","author":"Chen","year":"2018"},{"key":"10.1016\/j.sigpro.2026.110714_bib0024","unstructured":"N. Shlezinger, S. Segarra, Y. Zhang, D. Avrahami, Z. Davidov, T. Routtenberg, Y.C. Eldar, Deep Unfolding: Recent Developments, Theory, and Design Guidelines, (2025) arXiv: 2512.03768."},{"issue":"10","key":"10.1016\/j.sigpro.2026.110714_bib0025","doi-asserted-by":"crossref","first-page":"7418","DOI":"10.1109\/TGRS.2020.2982490","article-title":"Spectral mixture model inspired network architectures for hyperspectral unmixing","volume":"58","author":"Qian","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0026","first-page":"1","article-title":"ADMM-based hyperspectral unmixing networks for abundance and endmember estimation","volume":"60","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0027","first-page":"1","article-title":"SNMF-Net: learning a deep alternating neural network for hyperspectral unmixing","volume":"60","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0028","doi-asserted-by":"crossref","DOI":"10.3934\/ipi.2026032","article-title":"Total variation regularization based deep alternating neural network for blind unmixing of hyperspectral images","author":"Xie","year":"2026","journal-title":"Inverse Probl. Imaging"},{"key":"10.1016\/j.sigpro.2026.110714_bib0029","first-page":"1","article-title":"Unrolling plug-and-play network for hyperspectral unmixing","volume":"63","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0030","first-page":"1","article-title":"Sparsity-enhanced convolutional decomposition: a novel tensor-based paradigm for blind hyperspectral unmixing","volume":"60","author":"Yao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.sigpro.2026.110714_bib0031","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1137\/080733371","article-title":"Linearized Bregman iterations for frame-based image deblurring","volume":"2","author":"Cai","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"issue":"1","key":"10.1016\/j.sigpro.2026.110714_bib0032","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1137\/070703983","article-title":"Bregman iterative algorithms for \u21131-minimization with applications to compressed sensing","volume":"1","author":"Yin","year":"2008","journal-title":"SIAM J. Imaging Sci."},{"issue":"5","key":"10.1016\/j.sigpro.2026.110714_bib0033","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1111\/1365-2478.12762","article-title":"Curvelet reconstruction of non-uniformly sampled seismic data using the linearized Bregman method","volume":"67","author":"Zhang","year":"2019","journal-title":"Geophys. Prospect."},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0034","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3934\/ipi.2013.7.717","article-title":"Nonstationary iterated thresholding algorithms for image deblurring","volume":"7","author":"Huang","year":"2013","journal-title":"Inverse Probl. Imaging"},{"issue":"4","key":"10.1016\/j.sigpro.2026.110714_bib0035","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1137\/090760350","article-title":"Analysis and generalizations of the linearized Bregman method","volume":"3","author":"Yin","year":"2010","journal-title":"SIAM J. Imaging Sci."},{"issue":"9","key":"10.1016\/j.sigpro.2026.110714_bib0036","first-page":"2284","article-title":"Fast linearized Bregman method for compressed sensing","volume":"7","author":"Yang","year":"2013","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"10.1016\/j.sigpro.2026.110714_bib0037","series-title":"Proc. 1st Workshop Hyperspectral Image Signal Process., Evol. Remote Sens.","first-page":"1","article-title":"A variable splitting augmented Lagrangian approach to linear spectral unmixing","author":"Bioucas-Dias","year":"2009"},{"issue":"6","key":"10.1016\/j.sigpro.2026.110714_bib0038","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s10851-024-01208-8","article-title":"Mixing support detection-based alternating direction method of multipliers for sparse hyperspectral image unmixing","volume":"66","author":"Huang","year":"2024","journal-title":"J. Math. Imaging Vis."},{"issue":"4","key":"10.1016\/j.sigpro.2026.110714_bib0039","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1109\/TGRS.2018.2873326","article-title":"Joint-sparse-blocks and low-rank representation for hyperspectral unmixing","volume":"57","author":"Huang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.cam.2025.116787","article-title":"Overlapping patch-based joint-sparse regression for hyperspectral image unmixing","volume":"472","author":"Shu","year":"2026","journal-title":"J. Comput. Appl. Math."},{"key":"10.1016\/j.sigpro.2026.110714_bib0041","series-title":"2016 IEEE 7th Annu. Inf. Technol., Electron. and Mobile Commun. Conf. (IEMCON)","first-page":"1","article-title":"Sparse data reconstruction via adaptive \u2113p-norm and multilayer NMF","author":"Salehani","year":"2016"},{"issue":"1","key":"10.1016\/j.sigpro.2026.110714_bib0042","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0233-0","article-title":"Evaluation of maxout activations in deep learning across several big data domains","volume":"6","author":"Castaneda","year":"2019","journal-title":"J. Big Data."},{"key":"10.1016\/j.sigpro.2026.110714_bib0043","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"217","article-title":"A generalized iterated shrinkage algorithm for non-convex sparse coding","author":"Zuo","year":"2013"},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0044","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thresholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inf. Theory."},{"issue":"10","key":"10.1016\/j.sigpro.2026.110714_bib0045","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1162\/neco.2007.19.10.2756","article-title":"Projected gradient methods for nonnegative matrix factorization","volume":"19","author":"Lin","year":"2007","journal-title":"Neural Comput."},{"key":"10.1016\/j.sigpro.2026.110714_bib0046","series-title":"Matrix Analysis","author":"Horn","year":"2012"},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0047","first-page":"615","article-title":"An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems","volume":"6","author":"Toh","year":"2010","journal-title":"Pac. J. Optim."},{"key":"10.1016\/j.sigpro.2026.110714_bib0048","first-page":"1","article-title":"CyCU-Net: cycle-consistency unmixing network by learning cascaded autoencoders","volume":"60","author":"Gao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0049","first-page":"1","article-title":"UST-Net: a U-shaped transformer network using shifted windows for hyperspectral unmixing","volume":"61","author":"Yang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.sigpro.2026.110714_bib0050","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TGRS.2018.2872888","article-title":"Hyperspectral unmixing via total variation regularized nonnegative tensor factorization","volume":"57","author":"Xiong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"10.1016\/j.sigpro.2026.110714_bib0051","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1109\/TIP.2014.2363423","article-title":"Spectral unmixing via data-Guided sparsity","volume":"23","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.sigpro.2026.110714_bib0052","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2014.11.014","article-title":"Advanced radiometry measurements and earth science applications with the airborne prism experiment (APEX)","volume":"158","author":"Schaepman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.sigpro.2026.110714_bib0053","series-title":"Proc. 6th Workshop Hyperspectral Image Signal Process.: Evol. Remote Sens.","first-page":"1","article-title":"Validating nonlinear mixing models: benchmark datasets from vegetated areas","author":"Tits","year":"2014"},{"issue":"7","key":"10.1016\/j.sigpro.2026.110714_bib0054","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1080\/014311697218278","article-title":"Mineral mapping with hyperspectral digital imagery collection experiment (HYDICE) sensor data at cuprite, nevada, u.s.a","volume":"18","author":"Resmini","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0055","unstructured":"F. Zhu, Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey, (2017) arXiv: 1708.05125."},{"issue":"3","key":"10.1016\/j.sigpro.2026.110714_bib0056","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1109\/TGRS.2016.2633279","article-title":"Matrix-vector nonnegative tensor factorization for blind unmixing of hyperspectral imagery","volume":"55","author":"Qian","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.sigpro.2026.110714_bib0057","first-page":"1","article-title":"Supervised nonlinear hyperspectral unmixing with automatic shadow compensation using multiswarm particle swarm optimization","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165168426002288?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165168426002288?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:20:16Z","timestamp":1783074016000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165168426002288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":57,"alternative-id":["S0165168426002288"],"URL":"https:\/\/doi.org\/10.1016\/j.sigpro.2026.110714","relation":{},"ISSN":["0165-1684"],"issn-type":[{"value":"0165-1684","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LBNet: Linearized Bregman algorithm-based deep unfolding network for hyperspectral image unmixing","name":"articletitle","label":"Article Title"},{"value":"Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.sigpro.2026.110714","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":"110714"}}