Remove and recover: Deep end-to-end two-stage attention network for single-shot heavy rain removal
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Woo Jin | - |
dc.contributor.author | Kang, Tae Koo | - |
dc.contributor.author | Choi, Hyun Duck | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.date.accessioned | 2022-04-01T04:40:38Z | - |
dc.date.available | 2022-04-01T04:40:38Z | - |
dc.date.created | 2022-04-01 | - |
dc.date.issued | 2022-04-07 | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/139321 | - |
dc.description.abstract | In this paper, we propose a novel heavy rain removal algorithm using a deep neural network. Unlike most of the existing deraining methods, heavy rain removal is a more challenging task because it is necessary to remove both the rain marks and the haze effects, which are entangled in a complex manner. Motivated by this, we propose a new end-to-end two-stage attention network for single-shot heavy rain removal. The proposed network is connected serially with a removal network and a recovery network, which are based on a newly introduced dilation-wise attention block and skip attention block. Based on these attention techniques, the removal network predicts the heavy rain effect that needs to be removed from a given image, and the recovery network successfully predicts the details that need to be recovered, resulting in a clean image. We also introduce a new realistic RainCityscapes+ dataset, composed of synthesized outdoor images, and demonstrate extensive experiments, the results of which show our approach outperforms the state-of-the-art methods on both real and synthetic datasets quantitatively and qualitatively. (c) 2022 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | IMAGE | - |
dc.subject | RESTORATION | - |
dc.subject | VISIBILITY | - |
dc.subject | WEATHER | - |
dc.title | Remove and recover: Deep end-to-end two-stage attention network for single-shot heavy rain removal | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Myo Taeg | - |
dc.identifier.doi | 10.1016/j.neucom.2022.01.041 | - |
dc.identifier.scopusid | 2-s2.0-85123958881 | - |
dc.identifier.wosid | 000761817400001 | - |
dc.identifier.bibliographicCitation | NEUROCOMPUTING, v.481, pp.216 - 227 | - |
dc.relation.isPartOf | NEUROCOMPUTING | - |
dc.citation.title | NEUROCOMPUTING | - |
dc.citation.volume | 481 | - |
dc.citation.startPage | 216 | - |
dc.citation.endPage | 227 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | IMAGE | - |
dc.subject.keywordPlus | RESTORATION | - |
dc.subject.keywordPlus | VISIBILITY | - |
dc.subject.keywordPlus | WEATHER | - |
dc.subject.keywordAuthor | Image processing | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Image dehazing | - |
dc.subject.keywordAuthor | Image deraining | - |
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