Spatio-Temporal Transformer Network for Video Restoration
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Tae Hyun | - |
dc.contributor.author | Sajjadi, Mehdi S. M. | - |
dc.contributor.author | Hirsch, Michael | - |
dc.contributor.author | Schölkopf, Bernhard | - |
dc.date.accessioned | 2022-07-11T13:15:10Z | - |
dc.date.available | 2022-07-11T13:15:10Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/149463 | - |
dc.description.abstract | State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alleviate these problems, we propose a novel Spatio-temporal Transformer Network (STTN) which handles multiple frames at once and thereby manages to mitigate the common nuisance of occlusions in optical flow estimation. Our proposed STTN comprises a module that estimates optical flow in both space and time and a resampling layer that selectively warps target frames using the estimated flow. In our experiments, we demonstrate the efficiency of the proposed network and show state-of-the-art restoration results in video super-resolution and video deblurring. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Spatio-Temporal Transformer Network for Video Restoration | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Tae Hyun | - |
dc.identifier.doi | 10.1007/978-3-030-01219-9_7 | - |
dc.identifier.scopusid | 2-s2.0-85055119624 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.11207 LNCS, pp.111 - 127 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 11207 LNCS | - |
dc.citation.startPage | 111 | - |
dc.citation.endPage | 127 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Computer vision | - |
dc.subject.keywordPlus | Optical flows | - |
dc.subject.keywordPlus | Optical resolving power | - |
dc.subject.keywordPlus | Restoration | - |
dc.subject.keywordPlus | Deblurring | - |
dc.subject.keywordPlus | Optical flow estimation | - |
dc.subject.keywordPlus | Space and time | - |
dc.subject.keywordPlus | Spatio temporal | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Temporal information | - |
dc.subject.keywordPlus | Video restoration | - |
dc.subject.keywordPlus | Video super-resolution | - |
dc.subject.keywordPlus | Image reconstruction | - |
dc.subject.keywordAuthor | Spatio-temporal flow | - |
dc.subject.keywordAuthor | Spatio-temporal sampler | - |
dc.subject.keywordAuthor | Spatio-temporal transformer network | - |
dc.subject.keywordAuthor | Video deblurring | - |
dc.subject.keywordAuthor | Video super-resolution | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-030-01219-9_7 | - |
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