Deep Orthogonal Transform Feature for Image Denoising
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Yoon-Ho | - |
dc.contributor.author | Park, Min-Je | - |
dc.contributor.author | Lee, Oh-Young | - |
dc.contributor.author | Kim, Jong-Ok | - |
dc.date.accessioned | 2021-08-31T15:57:56Z | - |
dc.date.available | 2021-08-31T15:57:56Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58929 | - |
dc.description.abstract | Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce noise-separable orthogonal transform features into a neural denoising framework. We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally. In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network. For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method. This can play a role of prior for learning a denoising process. It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | WAVELET TRANSFORM | - |
dc.subject | SPARSE | - |
dc.title | Deep Orthogonal Transform Feature for Image Denoising | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Jong-Ok | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2986827 | - |
dc.identifier.scopusid | 2-s2.0-85084155713 | - |
dc.identifier.wosid | 000527415900010 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.66898 - 66909 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 66898 | - |
dc.citation.endPage | 66909 | - |
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.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | WAVELET TRANSFORM | - |
dc.subject.keywordPlus | SPARSE | - |
dc.subject.keywordAuthor | Image denoising | - |
dc.subject.keywordAuthor | deep learning for image denoising | - |
dc.subject.keywordAuthor | orthogonal transform | - |
dc.subject.keywordAuthor | multi-input network | - |
dc.subject.keywordAuthor | PCA | - |
dc.subject.keywordAuthor | wavelet transform | - |
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