{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:08:01Z","timestamp":1783930081410,"version":"3.55.0"},"reference-count":49,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Extracting spatio\u2010temporal cues from neighbouring frames is challenging in video super\u2010resolution (VSR). Although deformable alignment\u2010based VSR methods have shown promise in aligning neighbouring frames with the reference frame, most existing methods rely on one or a few traditional convolutions to estimate motion offsets for spatio\u2010temporal alignment, restricting receptive field size and alignment accuracy. To address these limitations, we propose an effective spatio\u2010temporal alignment network (ESTA\u2010Net) for VSR. The core component of our method is the group convolution\u2010based alignment module (GCBAM), which utilises cascaded group convolutions to learn offsets across both the original and downsampled resolutions. By employing group convolutions rather than traditional convolutions, GCBAM enables the deformable alignment to achieve a wider receptive field with lower computational cost, thereby improving the accuracy of offset estimation. Additionally, the bi\u2010scale alignment strategy within GCBAM enhances robustness to complex and large\u2010scale motions. Furthermore, we introduce an attention\u2010based feature enhancement module (AFEM) to refine the aligned features, focusing on critical details to improve reconstruction quality. Extensive experiments on standard benchmarks show that our ESTA\u2010Net achieves superior VSR performance against other advanced methods, while maintaining a good equilibrium between model size and performance.<\/jats:p>","DOI":"10.1049\/cit2.70151","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T10:22:13Z","timestamp":1780136533000},"page":"726-738","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video Super\u2010Resolution via Effective Spatio\u2010Temporal Alignment Network"],"prefix":"10.1049","volume":"11","author":[{"given":"Bin","family":"Guo","sequence":"first","affiliation":[{"name":"Konka Group Co. Ltd  Shenzhen China"},{"name":"Tsinghua Shenzhen International Graduate School Tsinghua University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7539-7784","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology, Shenzhen  Shenzhen China"},{"name":"Department of Computer Science City University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence Chongqing University of Technology  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1495-963X","authenticated-orcid":false,"given":"Yuhong","family":"Fu","sequence":"additional","affiliation":[{"name":"Konka Group Co. Ltd  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology, Shenzhen  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School Tsinghua University  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Future Media Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Harbin Institute of Technology, Shenzhen  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"e_1_2_9_2_1","article-title":"3dsrnet: Video Super\u2010Resolution Using 3d Convolutional Neural Networks","author":"Kim S. 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