{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T07:53:24Z","timestamp":1775894004501,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning. However, these methods are difficult to be applied in real-world scenarios since they are inevitably accompanied by the problems of computational and memory costs caused by the complex operations. To solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR. Specifically, an effective Symmetric CNN is designed for local feature extraction and coarse image reconstruction. Meanwhile, we propose a Recursive Transformer to fully learn the long-term dependence of images thus the global information can be fully used to further refine texture details. Studies show that the hybrid of CNN and Transformer can build a more efficient model. Extensive experiments have proved that our LBNet achieves more prominent performance than other state-of-the-art methods with a relatively low computational cost and memory consumption. The code is available at https:\/\/github.com\/IVIPLab\/LBNet.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/128","type":"proceedings-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T22:55:56Z","timestamp":1657925756000},"page":"913-919","source":"Crossref","is-referenced-by-count":117,"title":["Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer"],"prefix":"10.24963","author":[{"given":"Guangwei","family":"Gao","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"},{"name":"National Institute of Informatics"}]},{"given":"Zhengxue","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}]},{"given":"Juncheng","family":"Li","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"}]},{"given":"Yi","family":"Yu","sequence":"additional","affiliation":[{"name":"National Institute of Informatics"}]},{"given":"Tieyong","family":"Zeng","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T07:07:51Z","timestamp":1658128071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/128"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/128","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}