{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T19:59:38Z","timestamp":1783367978813,"version":"3.54.6"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["62271414"],"award-info":[{"award-number":["62271414"]}]},{"name":"National Natural Science Foundation of China under Grant","award":["LR23F010001"],"award-info":[{"award-number":["LR23F010001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China under Grant","award":["62271414"],"award-info":[{"award-number":["62271414"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China under Grant","award":["LR23F010001"],"award-info":[{"award-number":["LR23F010001"]}]},{"name":"Research Center for Industries of the Future (RCIF) at Westlake University","award":["62271414"],"award-info":[{"award-number":["62271414"]}]},{"name":"Research Center for Industries of the Future (RCIF) at Westlake University","award":["LR23F010001"],"award-info":[{"award-number":["LR23F010001"]}]},{"name":"Lochn Optics","award":["62271414"],"award-info":[{"award-number":["62271414"]}]},{"name":"Lochn Optics","award":["LR23F010001"],"award-info":[{"award-number":["LR23F010001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In order to capture the spatial-spectral (x,y,\u03bb) information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process, i.e., to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder\u2013decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms.<\/jats:p>","DOI":"10.3390\/e25040649","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T02:29:15Z","timestamp":1681352955000},"page":"649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2823-3901","authenticated-orcid":false,"given":"Siming","family":"Zheng","sequence":"first","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University, Hangzhou 310024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112321","DOI":"10.1016\/j.measurement.2022.112321","article-title":"3D spatial measurement for model reconstruction: A review","volume":"207","author":"Sergiyenko","year":"2023","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.opelre.2017.03.001","article-title":"Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods\u2019 approach","volume":"25","author":"Sergiyenko","year":"2017","journal-title":"Opto-Electron. 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