{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:10:33Z","timestamp":1760227833785,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T00:00:00Z","timestamp":1651190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61971233","62076137","KF2020Z-D01"],"award-info":[{"award-number":["61971233","62076137","KF2020Z-D01"]}]},{"name":"Henan Key Laboratory of Food Safety Data Intelligence","award":["61971233","62076137","KF2020Z-D01"],"award-info":[{"award-number":["61971233","62076137","KF2020Z-D01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the limited hardware conditions, hyperspectral image (HSI) has a low spatial resolution, while multispectral image (MSI) can gain higher spatial resolution. Therefore, derived from the idea of fusion, we reconstructed HSI with high spatial resolution and spectral resolution from HSI and MSI and put forward an HSI Super-Resolution model based on Spectral Smoothing prior and Tensor tubal row-sparse representation, termed SSTSR. Foremost, nonlocal priors are applied to refine the super-resolution task into reconstructing each nonlocal clustering tensor. Then per nonlocal cluster tensor is decomposed into two sub tensors under the tensor t-prodcut framework, one sub-tensor is called tersor dictionary and the other is called tensor coefficient. Meanwhile, in the process of dictionary learning and sparse coding, spectral smoothing constraint is imposed on the tensor dictionary, and L1,1,2 norm based tubal row-sparse regularizer is enforced on the tensor coefficient to enhance the structured sparsity. With this model, the spatial similarity and spectral similarity of the nonlocal cluster tensor are fully utilized. Finally, the alternating direction method of multipliers (ADMM) was employed to optimize the solution of our method. Experiments on three simulated datasets and one real dataset show that our approach is superior to many advanced HSI super-resolution methods.<\/jats:p>","DOI":"10.3390\/rs14092142","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"2142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hyperspectral Image Super-Resolution Method Based on Spectral Smoothing Prior and Tensor Tubal Row-Sparse Representation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"},{"name":"Henan Key Laboratory of Food Safety Data Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Qihao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9541-1894","authenticated-orcid":false,"given":"Zhiguo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"},{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Hyperspectral Image Mixed Denoising Using Difference Continuity-Regularized Nonlocal Tensor Subspace Low-Rank Learning","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE Geosci. 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