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However, existing subspace representation-based methods face challenges, including adaptive subspace dimension determination, inadequate spectral correlation capture, and expensive computation. In this paper, we propose a novel factor group sparsity regularized subspace representation (FGSSR)-based method for HSI super-resolution that can simultaneously address these issues encountered in previous methods. Specifically, by incorporating the factor group sparsity regularization into the subspace representation model, we first propose an FGSSR model to capture the spectral correlation property of the HR-HSI. The key advantage of FGSSR lies in its equivalence to the Schatten-p norm and its adaptive determination of the accurate subspace dimension, enabling it to capture spectral correlation more effectively. To preserve the spatial self-similarity prior in the HR-HSI, the tensor nuclear norm regularization on the low-dimensional coefficients is also incorporated into the proposed FGSSR-based model. Finally, an effective proximal alternating minimization-based algorithm is developed to solve the FGSSR-based model. Experimental results on the simulated and real datasets demonstrate that the proposed FGSSR-based method outperforms several state-of-the-art fusion methods with significant improvements.<\/jats:p>","DOI":"10.3390\/rs15194847","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T04:52:36Z","timestamp":1696827156000},"page":"4847","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hyperspectral Image Super-Resolution via Adaptive Factor Group Sparsity Regularization-Based Subspace Representation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3779-0360","authenticated-orcid":false,"given":"Yidong","family":"Peng","sequence":"first","affiliation":[{"name":"The Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9033-8245","authenticated-orcid":false,"given":"Weisheng","family":"Li","sequence":"additional","affiliation":[{"name":"The Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-0324","authenticated-orcid":false,"given":"Xiaobo","family":"Luo","sequence":"additional","affiliation":[{"name":"The Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Du","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119508","DOI":"10.1016\/j.eswa.2023.119508","article-title":"Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification","volume":"217","author":"Zhang","year":"2023","journal-title":"Expert Syst. 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