DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chung, Hyungjin | ko |
dc.contributor.author | Huh, Jaeyoung | ko |
dc.contributor.author | Kim, Geon | ko |
dc.contributor.author | Park, Yong Keun | ko |
dc.contributor.author | Ye, Jong Chul | ko |
dc.date.accessioned | 2021-08-18T04:30:08Z | - |
dc.date.available | 2021-08-18T04:30:08Z | - |
dc.date.created | 2021-08-18 | - |
dc.date.created | 2021-08-18 | - |
dc.date.created | 2021-08-18 | - |
dc.date.created | 2021-08-18 | - |
dc.date.created | 2021-08-18 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.747 - 758 | - |
dc.identifier.issn | 2573-0436 | - |
dc.identifier.uri | http://hdl.handle.net/10203/287199 | - |
dc.description.abstract | Optical diffraction tomography (ODT) produces a three-dimensional distribution of the refractive index (RI) by measuring scattering fields at various angles. Although the distribution of the RI is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along the axial direction compared to the horizontal imaging plane. To solve this issue, we present a novel unsupervised deep learning framework that learns the probability distribution of missing projection views through an optimal transport-driven CycleGAN. The experimental results show that missing cone artifacts in ODT data can be significantly resolved by the proposed method. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain | - |
dc.type | Article | - |
dc.identifier.wosid | 000682109300001 | - |
dc.identifier.scopusid | 2-s2.0-85112663783 | - |
dc.type.rims | ART | - |
dc.citation.volume | 7 | - |
dc.citation.beginningpage | 747 | - |
dc.citation.endingpage | 758 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING | - |
dc.identifier.doi | 10.1109/TCI.2021.3098937 | - |
dc.contributor.localauthor | Park, Yong Keun | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Optical diffraction tomography | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | unsupervised learning | - |
dc.subject.keywordAuthor | optimal transport | - |
dc.subject.keywordAuthor | CycleGAN | - |
dc.subject.keywordPlus | OPTICAL DIFFRACTION TOMOGRAPHY | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORK | - |
dc.subject.keywordPlus | PHASE MICROSCOPY | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | SUPERRESOLUTION | - |
dc.subject.keywordPlus | CYCLEGAN | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.