Accelerated Deconvolution microscopy based on iterative coordinate descent algorithm using GPU = GPU를 이용한 iterative coordinate descent 알고리즘 기반 고속 deconvolution microscopy

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In biological research, optical sectioning microscopy is widely used to observe the 3D structures of biological cells and tissues. However, the measured sectioning images are obscured by the out-of-focus information. To reduce such out-of-focus interference, it is necessary to implement a deconvolution method. Therefore, we employed iterative coordinate descent(ICD) algorithm which is based on updating of the pixel to optimize the statistical cost function iteratively. Even if the ICD method has rapidly convergent characteristic, the data complexity and the computational cost remain as limiting factors. To overcome this limitation, we apply a parallelized computing process occur in GPU hardware, known as general purpose computing on graphic processing units(GPGPU). In this thesis, we focus on implementation of the ICD algorithm based on the GPU architecture. From simulation data, we verify the performance by testing several realization of the proposed algorithm.
Advisors
Ye, Jong-Chulresearcher예종철researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2010
Identifier
418986/325007  / 020083280
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2010.2, [ vii, 33 p. ]

Keywords

GPU; Deconvolution; ICD; 영상복원; 현미경; 디컨볼루션; Microscopy

URI
http://hdl.handle.net/10203/27190
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=418986&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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