DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Jihyong | ko |
dc.contributor.author | Kim, Munchurl | ko |
dc.date.accessioned | 2022-12-05T01:00:58Z | - |
dc.date.available | 2022-12-05T01:00:58Z | - |
dc.date.created | 2022-12-02 | - |
dc.date.created | 2022-12-02 | - |
dc.date.issued | 2022-10-25 | - |
dc.identifier.citation | European Conference on Computer Vision, ECCV 2022, pp.1 - 17 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10203/301577 | - |
dc.description.abstract | We propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework in a two-stage manner, called DeMFINet, which converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI). Its baseline version performs featureflow-based warping with FAC-FB module to obtain a sharp-interpolated frame as well to deblur two center-input frames. Its extended version further improves the joint performance based on pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively gathers the distributed blurry pixel information over blurry input frames in featuredomain to improve the joint performances. RB trained with recursive boosting loss enables DeMFI-Net to adequately select smaller RB iterations for a faster runtime during inference, even after the training is finished. As a result, our DeMFI-Net achieves state-of-the-art (SOTA) performances for diverse datasets with significant margins compared to recent joint methods. All source codes, including pretrained DeMFI-Net, are publicly available at https://github.com/JihyongOh/DeMFI. | - |
dc.language | English | - |
dc.publisher | European Conference on Computer Vision | - |
dc.title | DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting | - |
dc.type | Conference | - |
dc.identifier.wosid | 000897035700012 | - |
dc.identifier.scopusid | 2-s2.0-85142761611 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 1 | - |
dc.citation.endingpage | 17 | - |
dc.citation.publicationname | European Conference on Computer Vision, ECCV 2022 | - |
dc.identifier.conferencecountry | IS | - |
dc.identifier.conferencelocation | Tel Aviv | - |
dc.identifier.doi | 10.1007/978-3-031-20071-7_12 | - |
dc.contributor.localauthor | Kim, Munchurl | - |
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