Browsing by Author "Su, Jian-Jia"

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A skew freeform reflector design method for prescribed off-axis irradiance
(2019-06-01)
Su, Jian-Jia
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Tien, Chung-Hao
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Tsai, Yu-Lin
;
Wu, Chin-Tien
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應用數學系
In this paper we proposed a new methodology for designing freeform reflectors tailored to redistribute the radiant flux emitted by a point light source onto a target plane with the prescribed irradiance pattern. Based on the laws of reflection and energy conservation, a skew freeform model with a set of second-order ordinary differential equations formulating the ray tracing between the source and target domain was presented. A 3D freeform reflector was constructed by using such skew freeform model with quadric surface fitting accordingly. Proposed scheme is applicable for arbitrary geometric-based mapping without the restrictions subject to the target, source, collective solid angle, or target topography, respectively.
Accessing Refractive Errors via Eccentric Infrared Photorefraction Based on Deep Learning
(2019-01-01)
Yang, Chia-Chi
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Su, Jian-Jia
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Li, Jie-En
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Zhu, Zhi-Yu
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Tseng, Jin-Shing
Eccentric infrared photorefraction is an attractive vision screening method which is widely used for uncooperative subjects, such as infants and toddlers. Unlike conventional slope-based photorefraction, a deep neural network is used to predict refractive error in this study. Total 1216 ocular image were collected by a homemade photorefraction device, whose corresponding refractive error was measured by a commercial autorefractor device, to create a series of dataset for our deep neural network. The mean squared error of the preliminary result is +/- 0.9 diopter, which indicates its feasibility and can be improved with bigger database.
Automatic Organic Light-emitting Diode Display Mura Detection Model based on Human Visual Perception and Multi-resolution
(2019-01-01)
Zhu, Zhi-Yu
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Li, Jie-En
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Hsieh, Po-Yuan
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Su, Jian-Jia
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Tien, Chung-Hao
Organic light emitting diode generally has serious non-uniformity phenomena due to the instability of organic processing, called Mura. In this paper, we propose an automatic Mura detection model to mimic the human perception and detect Mura pixel-wisely. First, we extract regions of interest from the original image with different sizes of windows, and then we verify these regions by SEMU criterion. Consequently, we implement human visual properties based on the contrast sensitivity function filtering and ModelFest matching to segment Mura regions. As the result, our approach can successfully detect Mura with various sizes and shapes, which could have a great impact on the display industry.
Improved Error Reduction and Hybrid Input Output Algorithms for Phase Retrieval by including a Sparse Dictionary Learning-Based Inpainting Method
(2020-07-20)
Su, Jian-Jia
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Tien, Chung-Hao
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光電工程學系
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Department of Photonics
The phase retrieval (PR), reconstructing an object from its Fourier magnitudes, is equivalent to a nonlinear inverse problem. In this paper, we proposed a two-step algorithm that traditional ER/HIO iteration plays as the coarse feature reconstruction, whereas the KSVD-based inpainting technique deals with the fine feature set accordingly. Since the KSVD allows the content of oversampled dictionary with sparse representation to adaptively fit a given set of object examples, as long as the ER/HIO algorithms provide decent object estimation at early stage, the pixels violating the object constraint can be restored with superior image quality. The numerical analyses demonstrated the effectiveness of ER + KSVD and HIO + KSVD through multiple independent initial Fourier phases. With its versatility and simplicity, the proposed method can be generalized to be implemented with more PR state-of-the-arts.
Recovery of Phase Modulation via Residual Neural Network
(2019-01-01)
Yao, Yun-Zhen
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Su, Jian-Jia
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Li, Jie-En
;
Zhu, Zhi-Yu
;
Tien, Chung-Hao
An approach for recovering the phase information from the detected intensity was proposed in this work. Unlike the conventional approach based on the Gerchberg-Saxton algorithm, the proposed approach recovered the phase information via an alternative technique in the realm of deep learning, the residual neural network. The database we utilized to train the network was collected by a Michelson-based interferometer, where a spatial light modulator was implemented to provide the phase modulation as the phase object. As the result, the mean absolute error of each pixel was 0.0614 pi.