Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kanggeun | - |
dc.contributor.author | Jeong, Won-Ki | - |
dc.date.accessioned | 2022-08-13T07:40:17Z | - |
dc.date.available | 2022-08-13T07:40:17Z | - |
dc.date.created | 2022-08-12 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143004 | - |
dc.description.abstract | With the advent of unsupervised learning, efficient training of a deep network for image denoising without pairs of noisy and clean images has become feasible. Most current unsupervised denoising methods are built on self-supervised loss with the assumption of zero-mean noise under the signal-independent condition, which causes brightness-shifting artifacts on unconventional noise statistics (i.e., different from commonly used noise models). Moreover, most blind denoising methods require a random masking scheme for training to ensure the invariance of the denoising process. In this study, we propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking. We also propose an adaptive self-supervision loss to increase the tolerance for unconventional noise, which is specifically effective in removing salt-and-pepper or hybrid noise where prior knowledge of noise statistics is not readily available. We demonstrate the efficacy of the proposed method by comparing it with state-of-the-art denoising methods using various examples. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | TOTAL VARIATION MINIMIZATION | - |
dc.subject | IMAGE | - |
dc.subject | SPARSE | - |
dc.subject | CNN | - |
dc.title | Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jeong, Won-Ki | - |
dc.identifier.doi | 10.3390/s22114255 | - |
dc.identifier.scopusid | 2-s2.0-85131187804 | - |
dc.identifier.wosid | 000808648500001 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.11 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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.subject.keywordPlus | TOTAL VARIATION MINIMIZATION | - |
dc.subject.keywordPlus | IMAGE | - |
dc.subject.keywordPlus | SPARSE | - |
dc.subject.keywordPlus | CNN | - |
dc.subject.keywordAuthor | blind denoising | - |
dc.subject.keywordAuthor | self-supervision | - |
dc.subject.keywordAuthor | adaptive loss | - |
dc.subject.keywordAuthor | J-invariant network | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.