{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:26:44Z","timestamp":1776886004453,"version":"3.51.2"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2017YFC0403302"],"award-info":[{"award-number":["2017YFC0403302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we propose a new hyperspectral image (HSI) denoising model with the group sparsity regularized hybrid spatio-spectral total variation (GHSSTV) and low-rank tensor decomposition, which is based on the analysis of structural sparsity of HSIs. First, the global correlations among all modes are explored by the Tucker decomposition, which applies low-rank constraints to the clean HSIs. To avoid over-smoothing, we propose GHSSTV regularization to ensure the group sparsity not only in the first-order gradient domain but also in the second-order ones along the spatio-spectral dimensions. Then, the sparse noise in HSI can be detected by the \u21131 norm. Furthermore, strong Gaussian noise is simulated by the Frobenius norm. The alternating direction multiplier method (ADMM) algorithm is employed to effectively solve the GHSSTV model. Finally, experimental results from a series of simulations and real-world data suggest a superior performance of the GHSSTV method in HSIs denoising.<\/jats:p>","DOI":"10.3390\/rs14102348","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hyperspectral Image Denoising via Group Sparsity Regularized Hybrid Spatio-Spectral Total Variation"],"prefix":"10.3390","volume":"14","author":[{"given":"Pengdan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jifeng","family":"Ning","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Northwest A&F University, Xianyang 712000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Trans. 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