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dc.contributor.advisor 유재석 -
dc.contributor.author Dongkyu Jung -
dc.date.accessioned 2022-08-17T16:00:10Z -
dc.date.available 2022-08-17T16:00:10Z -
dc.date.issued 2022 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000629151 en_US
dc.identifier.uri http://hdl.handle.net/20.500.11750/16788 -
dc.description Ultrasound, Speckle, Unsupervised learning, Deep learning -
dc.description.abstract -
dc.description.statementofresponsibility N -
dc.description.tableofcontents 1. INTRODUCTION 1
2. METHODS 5
2.1. Speckle-to-Speckle: Unsupervised learning-based framework 5
2.1.1. Background 5
2.1.2. Speckle-to-Speckle network design 7
2.1.3. Training and inference 8
2.2. Experimental setup 9
2.2.1. Data acquisition 9
2.2.2. Implementation details 11
2.2.3. Evaluation metrics 11
3. RESULTS 15
3.1. Evaluation using training dataset 15
3.2. Evaluation using validation dataset 17
3.3. Evaluation using unspecified dataset 18
3.4. Calculation complexity 20
4. DISCUSSIONS 22
5. CONCLUSION 24
6. REFERENCE 25
요약문 27
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dc.format.extent 27 -
dc.language eng -
dc.publisher DGIST -
dc.subject Ultrasound, Speckle, Unsupervised learning, Deep learning -
dc.title Speckle-to-Speckle: Ultrasound speckle reduction technique using unsupervised deep learning -
dc.type Thesis -
dc.identifier.doi 10.22677/thesis.200000629151 -
dc.description.degree Master -
dc.contributor.department Department of Robotics and Mechatronics Engineering -
dc.contributor.coadvisor Sangyoon Han -
dc.date.awarded 2022/08 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.RM 정25 202208 -
dc.date.accepted 8/11/22 -
dc.contributor.alternativeDepartment 로봇및기계전자공학과 -
dc.embargo.liftdate 2023-08-31 -
dc.contributor.affiliatedAuthor Dongkyu Jung -
dc.contributor.affiliatedAuthor Jaesok Yu -
dc.contributor.affiliatedAuthor Sangyoon Han -
dc.contributor.alternativeName 정동규 -
dc.contributor.alternativeName Jaesok Yu -
dc.contributor.alternativeName 한상윤 -
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