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Enhancement of Perivascular Spaces using 3D Convolutional Neural Network

Title
Enhancement of Perivascular Spaces using 3D Convolutional Neural Network
Translated Title
3차원 합성곱 신경망을 이용한 혈관영상 화질 향상
Authors
Euijin Jung
DGIST Authors
Euijin Jung
Advisor(s)
박상현
Co-Advisor(s)
Jaeil Kim
Issue Date
2019
Available Date
2019-10-03
Degree Date
2019-02
Type
Thesis
Table Of Contents
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
URI
http://dgist.dcollection.net/common/orgView/200000171501
http://hdl.handle.net/20.500.11750/10757
DOI
10.22677/thesis.200000171501
Degree
MASTER
Department
Robotics Engineering
University
DGIST
Files:
There are no files associated with this item.
Collection:
Department of Robotics EngineeringETCETC


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