Multi-energy radiograph and facial expression image synthesis using collaborative generative adversarial network = 다중 도메인 협력 적대적 생성 신경망 기반 다중에너지 방사선영상 합성 및 표정 영상 생성 연구

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Collaborative generative adversarial network (CollaGAN) is the algorithm recently proposed that is for multi-domain image translation or missing data imputation. This study expands the CollaGAN for the two different tasks with other methods for each applications. First application is the multi-energy chest radiograph bone subtraction. We propose the network of CollaGAN that can synthesize ridograph of unmeasured energy level using multi-energy radiographs. After that, we proposed eigenvalue decomposition method that decompose bone and soft tissue images from multi-energy radiographs. Also, the noise of the image is decreased as a post-processing by using wavelet cycle consistency generative adversarial network. Experimental results and qualitative evaluation verify the method effectively results in bone-removed chest radiographs. Second part of the study is about the facial expression images translation by using registration network and the GANs. For two arbitrary face images, it is difficult to train GANs alone for image translation. In this study, We introduce the registration network for the first step deformation. And then, unnatural deformed images are refined to be more realistic by CollaGAN.
Advisors
Ye, Jong Chulresearcher예종철researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.8,[iv, 36 p. :]

Keywords

generative adversarial network▼achest radiograph▼abone subtraction▼aeigenvalue decomposition▼aregistration▼afacial expression image▼aimage translation; 적대적 생성 신경망▼a흉부 방사선영상▼a뼈 소거▼a고유 값 분해▼a정합▼a얼굴 표정 영상▼a영상 변환

URI
http://hdl.handle.net/10203/284928
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925087&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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