Hint-Based Image Colorization Based on Hierarchical Vision Transformer
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Subin | - |
dc.contributor.author | Jung, Yong Ju | - |
dc.date.accessioned | 2022-11-11T07:40:26Z | - |
dc.date.available | 2022-11-11T07:40:26Z | - |
dc.date.created | 2022-11-08 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86026 | - |
dc.description.abstract | Hint-based image colorization is an image-to-image translation task that aims at creating a full-color image from an input luminance image when a small set of color values for some pixels are given as hints. Though traditional deep-learning-based methods have been proposed in the literature, they are based on convolution neural networks (CNNs) that have strong spatial locality due to the convolution operations. This often causes non-trivial visual artifacts in the colorization results, such as false color and color bleeding artifacts. To overcome this limitation, this study proposes a vision transformer-based colorization network. The proposed hint-based colorization network has a hierarchical vision transformer architecture in the form of an encoder-decoder structure based on transformer blocks. As the proposed method uses the transformer blocks that can learn rich long-range dependency, it can achieve visually plausible colorization results, even with a small number of color hints. Through the verification experiments, the results reveal that the proposed transformer model outperforms the conventional CNN-based models. In addition, we qualitatively analyze the effect of the long-range dependency of the transformer model on hint-based image colorization. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Hint-Based Image Colorization Based on Hierarchical Vision Transformer | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000867281200001 | - |
dc.identifier.doi | 10.3390/s22197419 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.19 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85139811056 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 19 | - |
dc.contributor.affiliatedAuthor | Lee, Subin | - |
dc.contributor.affiliatedAuthor | Jung, Yong Ju | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | image colorization | - |
dc.subject.keywordAuthor | vision transformer | - |
dc.subject.keywordAuthor | attention map | - |
dc.subject.keywordAuthor | deep learning | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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