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dc.contributor.advisor 박상현 -
dc.contributor.author Euijin Jung -
dc.date.accessioned 2019-10-02T16:04:31Z -
dc.date.available 2019-10-02T16:04:31Z -
dc.date.issued 2019 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000171501 en_US
dc.identifier.uri http://hdl.handle.net/20.500.11750/10757 -
dc.description.statementofresponsibility prohibition -
dc.description.tableofcontents Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

I. INTRODUCTION 1

II. Related Works 3
1 Spatial Domain Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Transform Domain Approaches . . . . . . . . . . . . . . . . . . . . . . . 3
3 Learning Based Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 4
4 Medical Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . 4

III. METHODS 6
1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 SRCNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 VDSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 Dense Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5 Densely Connected Dense Network . . . . . . . . . . . . . . . . . . . . . 9

IV. RESULTS 10
1 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Discussion for comparison networks . . . . . . . . . . . . . . . . . . . . . 15
6 Discussion for network depth . . . . . . . . . . . . . . . . . . . . . . . . . 18

V. CONCLUSION 20

References 21
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dc.format.extent 35 -
dc.language eng -
dc.publisher DGIST -
dc.source /home/dspace/dspace53/upload/200000171501.pdf -
dc.title Enhancement of Perivascular Spaces using 3D Convolutional Neural Network -
dc.title.alternative 3차원 합성곱 신경망을 이용한 혈관영상 화질 향상 -
dc.type Thesis -
dc.identifier.doi 10.22677/thesis.200000171501 -
dc.description.degree MASTER -
dc.contributor.department Robotics Engineering -
dc.contributor.coadvisor Jaeil Kim -
dc.date.awarded 2019-02 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.RM 정67 201902 -
dc.date.accepted 2019-01-30 -
dc.contributor.alternativeDepartment 로봇공학전공 -
dc.embargo.liftdate 2021-02-01 -
dc.contributor.affiliatedAuthor Park, Sanghyun -
dc.contributor.affiliatedAuthor Jung, Euijin -
dc.contributor.affiliatedAuthor Kim, Jaeil -
dc.contributor.alternativeName Sang Hyun Park -
dc.contributor.alternativeName 정의진 -
dc.contributor.alternativeName 김재일 -
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Department of Robotics and Mechatronics Engineering Theses Master

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