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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1137
DC FieldValueLanguage
dc.contributor.authorKhoa Van Tranen_US
dc.contributor.authorDinh Quang Vinhen_US
dc.contributor.authorPhuc Hong Nguyenen_US
dc.contributor.authorNarayan DEBNATH Cen_US
dc.contributor.authorNguyen Tuan Ducen_US
dc.contributor.authorChang Wook Ahnen_US
dc.date.accessioned2021-08-14T11:49:43Z-
dc.date.available2021-08-14T11:49:43Z-
dc.date.issued2020-
dc.identifier.citationhttp://manuscriptlink-society-file.s3.amazonaws.com/kism/conference/sma2020/presentation/SMA-2020_paper_39.pdfen_US
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/1137-
dc.description.abstractThis paper proposes a deep neural network that solves the denoising and colorization problem simultaneously. The joint problem is solved by two separate sub-networks that are trained in an end-to-end manner. Specifically, map attention modules are used to revise feature maps, while a few convolutional layers to extract features at the beginning of the network helps to boost the proposed network significantly. We use KITTI dataset to prepare training and testing datasets. In addition, we compare the proposed method with the baseline method using the PSNR and SSIM metrics. To have a fair comparison, we train the proposed and baseline methods using the same dataset, loss function, and training configurations. The experimental results show that the proposed method performed significantly better the baseline method in the KITTI dataset-
dc.language.isoenen_US
dc.subjectImage denoising-
dc.subjectColorization-
dc.subjectDeep network-
dc.titleEnd-to-end deep network for image denoising and colorizationen_US
dc.typeConference Paperen_US
dc.relation.conferenceThe 9th International Conference on Smart Media and Application-
dc.relation.duration17-19/9/2020-
dc.relation.conferencevenueJeju, Korea-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
Appears in Collections:Conference papers
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