DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Yoon, Young-Gyu | - |
dc.contributor.advisor | 윤영규 | - |
dc.contributor.author | Shin, Changyeop | - |
dc.date.accessioned | 2023-06-26T19:34:27Z | - |
dc.date.available | 2023-06-26T19:34:27Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1008350&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309983 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2022.8,[iv, 21 p. :] | - |
dc.description.abstract | Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we introduce a computational microscopy technique, based on a recursive light propagation network (RLP-Net), that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Fluorescence microscopy▼avirtual refocusing▼a3-D volume estimation▼arecursive neural network▼arecursive inference | - |
dc.subject | 형광 현미경▼a가상 재초점▼a3차원 영상복원▼a재귀 신경망▼a재귀 추론 | - |
dc.title | Three-dimensional fluorescence microscopy through virtual refocusing using a recursive light propagation network | - |
dc.title.alternative | 3차원 형광영상 복원을 위한 딥러닝 기반 가상 재초점 기술 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 신창엽 | - |
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