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Appl."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>Video deblurring methods exploit the correlation between consecutive blurry inputs to generate sharp frames. However, designing an effective and efficient method is a challenging problem for video deblurring. To guarantee the effectiveness and further improve the deblurring performance, we adopt the recurrent-based method as the baseline and reconsider the recurrent mechanism as well as the temporal feature alignment in the state-of-the-art methods. For the recurrent mechanism, we add the local backward connection to the global forward recurrent backbone to effectively exploit accurate future information. For the temporal alignment, we adopt a fused temporal merge module that exploits the superiority of flow-based and kernel-based methods with progressive correlation volumes estimation. In addition, we evaluate our method with both synthetic datasets (GoPro, DVD) and a realistic dataset (BSD). The experimental results demonstrate that our method achieves significant performance improvement with a slight computational cost increase against the state-of-the-art video deblurring methods. The extended ablation studies verify the effectiveness of our model.<\/jats:p>","DOI":"10.1145\/3587468","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T14:02:56Z","timestamp":1678716176000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Local Bidirection Recurrent Network for Efficient Video Deblurring with the Fused Temporal Merge Module"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4059-2058","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7124-5182","authenticated-orcid":false,"given":"Li","family":"Song","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8261-5337","authenticated-orcid":false,"given":"Rong","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8799-1182","authenticated-orcid":false,"given":"Wenjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00382"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00491"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00588"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185560"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2015.2501245"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3408293"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547874"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.392"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.348"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299181"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.435"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2761348"},{"key":"e_1_3_3_14_2","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. 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