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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            In recent years, thanks to the inherent powerful feature representation and learning abilities of the convolutional neural network (CNN), deep CNN-steered single image super-resolution approaches have achieved remarkable performance improvements. However, these methods are often accompanied by large consumption of computing and memory resources, which is difficult to be adopted in real-world application scenes. To handle this issue, we design an efficient Feature De-redundancy and Self-calibration Super-resolution network (FDSCSR). In particular, a Feature De-redundancy and Self-calibration Block (FDSCB) is proposed to reduce the repetitive feature information extracted by the model and further enhance the efficiency of the model. Then, based on FDSCB, a Local Feature Fusion Module is presented to elaborately utilize and fuse the feature information extracted by each FDSCB. Abundant experiments on benchmarks have demonstrated that our FDSCSR achieves superior performance with relatively less computational consumption and storage resource than other state-of-the-art approaches. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/IVIPLab\/FDSCSR\">https:\/\/github.com\/IVIPLab\/FDSCSR<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3569900","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T11:05:12Z","timestamp":1667041512000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Lightweight Feature De-redundancy and Self-calibration Network for Efficient Image Super-resolution"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4668-2559","authenticated-orcid":false,"given":"Zhengxue","family":"Wang","sequence":"first","affiliation":[{"name":"College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Qixai District, Nanjing City, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3950-1844","authenticated-orcid":false,"given":"Guangwei","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Advanced Technology for Carbon Neutrality, Nanjing University of Posts and Telecommunications, Qixai District, Nanjing City, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7314-6754","authenticated-orcid":false,"given":"Juncheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Baoshan District, Shanghai City, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8332-9255","authenticated-orcid":false,"given":"Hui","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Xuanwu District, Nanjing City, 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-9660","authenticated-orcid":false,"given":"Hao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Nanjing Xiaozhuang University, Nanjing City, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9794-3221","authenticated-orcid":false,"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu City, Japan"}]}],"member":"320","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"126","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW\u201917)","author":"Agustsson Eirikur","year":"2017","unstructured":"Eirikur Agustsson and Radu Timofte. 2017. 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