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15:15 - 15:30
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Award Candidate (Paper Competition)
Manuscript ID. 0496
Paper No. 2021-THU-S0402-O001
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| Huai-Ming Kuan
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Hyperspectral System used for multi-point measurements
Huai-Ming Kuan;Yeh-Wei Yu;Wen-Hsuan Wu;Tsung-Hsun Yang;Ching-Cherng Sun
Nowadays, hyperspectral imaging systems are costly and difficult to produce. Besides, the measurement is extremely time-consuming, which is not conducive to the rapidly changing scientific measurement. In this paper, we proposed a hyperspectral imaging system with reasonable production cost and fast measurement speed
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15:30 - 15:45
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Award Candidate (Paper Competition)
Manuscript ID. 0662
Paper No. 2021-THU-S0402-O002
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| Yun-hsiu Lee
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Synthesis of poly(ethylene glycol phenyl ether acrylate photopolymers for holographic storage
Yun-hsiu Lee;Fang-Yong Lee;Tzu-Chien Hsu;Wei-Hung Su
A series of photopolymers based on the ethylene glycol phenyl ethyl arylate (EGPEA) monomers with varying initiator concentration and sample thickness is synthesized. The advantages of lowering the initiator concentration, including a rather short initiation time within few seconds and a sharp rising optical response, are demonstrated.
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15:45 - 16:00
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Award Candidate (Paper Competition)
Manuscript ID. 0692
Paper No. 2021-THU-S0402-O003
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| Hung Chuan Hsu
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Resolution improvement in light-sheet fluorescence microscopy using Richardson-Lucy deconvolution
Hung Chuan Hsu;Sunil Vyas;Kuang Yuh Huang;Hsien Shun Liao;Yuan Luo
Airy beam light-sheet microscopy offer advantages in providing better optical sectioning capability, longer field of view, and deeper penetration depth. With the deconvolution method, contrast and resolution in images can be improved by removing out-of-focus noise. Here, we demonstrated Airy light-sheet microscopy and used the Richardson-Lucy deconvolution method to further improve the image quality.
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16:00 - 16:15
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Manuscript ID. 0585
Paper No. 2021-THU-S0402-O004
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| Zih Fan Chen
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Deep Learning Neural Network for Computer Hologram Generation
Zih Fan Chen;Shiuan Huei Lin;Vera Marinova;Ken Y Hsu
In this paper, we present the feasibility of computer generated hologram (CGH) by Neural Network. In our method, CGH is done by autoencoder framework and the training dataset of the deep learning model is created by Iterative Fourier Transform Algorithm (IFTA). The method enables the non-iterative calculation of CGH for real-time applications.
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16:15 - 16:30
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Manuscript ID. 0271
Paper No. 2021-THU-S0402-O005
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| Liang-Wei Chen
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3D Micropatterned Multiphoton Stimulation via Deep Learning-Based Computer-Generated Holography with Temporal Focusing Confinement
Liang-Wei Chen;Hua-Wei Ku;Feng-Chun Hsu;Chun-Yu Lin;Yvonne Yuling Hu;Shean-Jen Chen
Multiphoton excitation can reduce tissue scattering and get deeper image depth. With optogenetics, neural activity can be modulated to influence biological behavior. Precisely spatial and temporal control the shape of lights is important to observe the 3D distributed neural connection. In this study, we use a deep-learning based computer-generated holography to generate holograms with orders of magnitude faster and better accuracy. Furthermore, to improve axial resolution of the stimulated pattern, we introduce temporal focusing (TF) to get a better axial confinement. With all the applied technology, we can real-time and precisely stimulate drosophila brain in single-cell resolution.
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16:30 - 16:45
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Manuscript ID. 0382
Paper No. 2021-THU-S0402-O006
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| Yi-Yung Chen
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Phase Retrieval of Interference Fringe based on Generative Adversarial Network
Cheng-Tse Lin;Yi-Yung Chen
We used generative adversarial networks (GAN) to help phase retrieval from interferometry. The established image is transformed from fringe pattern to phase map. They can be fitted by Zernike polynomial and reducing the calculating steps.
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