{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:23:01Z","timestamp":1768908181518,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11571325"],"award-info":[{"award-number":["11571325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CUC2019 A002"],"award-info":[{"award-number":["CUC2019 A002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["11571325"],"award-info":[{"award-number":["11571325"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CUC2019 A002"],"award-info":[{"award-number":["CUC2019 A002"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Effective aggregation of temporal information of consecutive frames is the core of achieving video super-resolution. Many scholars have utilized structures such as sliding windows and recurrences to gather the spatio-temporal information of frames. However, although the performances of constructed video super-resolution models are improving, the sizes of the models are also increasing, exacerbating the demand on the equipment. Thus, to reduce the stress on the device, we propose a novel lightweight recurrent grouping attention network. The parameters of this model are only 0.878 M, which is much lower than the current mainstream model for studying video super-resolution. We have designed a forward feature extraction module and a backward feature extraction module to collect temporal information between consecutive frames from two directions. Moreover, a new grouping mechanism is proposed to efficiently collect spatio-temporal information of the reference frame and its neighboring frames. The attention supplementation module is presented to further enhance the information gathering range of the model. The feature reconstruction module aims to aggregate information from different directions to reconstruct high-resolution features. Experiments demonstrate that our model achieves state-of-the-art performance on multiple datasets.<\/jats:p>","DOI":"10.3390\/s23208574","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"8574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0048-2084","authenticated-orcid":false,"given":"Yonggui","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-781X","authenticated-orcid":false,"given":"Guofang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/TBC.2022.3147145","article-title":"Cross-Frame Transformer-Based Spatio-Temporal Video Super-Resolution","volume":"68","author":"Zhang","year":"2022","journal-title":"IEEE Trans. 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