• Korean Journal of Optics and Photonics
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• v.20 no.2
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• pp.76-80
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• 2009

Finger-print recognition is achieved by comparing an input finger-print image with the stored images in the computer, and finally by determining agreement or disagreement. Encryption and decryption are necessary in the finger-print recognition process. In these process CGH (Computer Generated Hologram) is used, and finger-print images reconstructed from the CGHs are compared. In this paper, two methods of recognition are used, one is to compare the finger-print images of each other reconstructed from their CGHs and the other is to compare the CGHs to each other directly, to analyze the differences of finger-print recognition capability between these two methods. Experimental results show that the capability of finger-print recognition for comparing the CGHs of the two is about 150 times higher than in case of comparing the reconstructed finger-print images. Especially the changes of characteristics according to modulation types of CGH are analyzed.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.3-20
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• 2023

A computer-generated hologram (CGH) is a digitally calculated and recorded hologram in which the amplitude and phase information of an image is transmitted in free space. The CGH is in the form of a complex hologram, but it is converted into a phase-only hologram to display through a phase-only spatial light modulator (SLM). In this paper, in the process of including the amplitude information of an object in the phase information, when a technique that includes subsampling such as DPAC is used, we showed experimentally that the bandwidth of the phase-only hologram increases, and as a result, aliasing that was not present in the complex hologram can occur. In addition, it was experimentally shown that it is possible to generate a high-quality phase-only hologram by restricting the spatial frequency range even at a distance where the numerical reconstruction performance is degraded by aliasing.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.92-97
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• 2009

In this paper, a conventional simulated annealing (SA) method for optimization of a kinoform computer generated hologram (CGH) is analyzed and the SA method is modified to reduce a reconstruction error rate (ER) of the CGH. The dependences of the quantization level of the hologram pattern and the size of the data on the ER are analyzed. To overcome saturation of the ER, the conventional SA method is modified as it magnifies a Fourier-transformed pattern in the intermediate step. The proposed method can achieve a small ER less than 1%, which is impossible in the conventional SA method.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.767-774
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• 2005

In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video images. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. The proposed algorithm illustrated that it have better properties for reconstruction and compression rate than the previous methods.

• Journal of Information Display
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• v.10 no.1
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• pp.6-15
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• 2009

In this paper, a new approach is proposed for the efficient generation of computer-generated holograms (CGHs) using the spatially redundant data on a 3D object and the novel look-up table (N-LUT) method. First, the pre-calculated N-point principle fringe patterns (PFPs) were calculated using the 1-point PFP of the N-LUT. Second, spatially redundant data on a 3D object were extracted and re-grouped into the N-point redundancy map using the run-length encoding (RLE) method. Then CGH patterns were generated using the spatial redundancy map and the N-LUT method. Finally, the generated hologram patterns were reconstructed. In this approach, the object points that were involved in the calculation of the CGH patterns were dramatically reduced, due to which the computational speed was increased. Some experiments with a test 3D object were carried out and the results were compared with those of conventional methods.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.452-462
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• 2019

A digital hologram, which is one of the next generation visual systems, can be generated and displayed in various formats, and a digital hologram is created in accordance with the characteristics of the system for display. Diffraction efficiency can be used as a measure of the characteristics of digital holograms generaged under various conditions in various display environments. In this paper, diffraction efficiency for computer-generated hologram (CGH) under various conditions was measured. This paper discusses the generation conditions that should be considered in hologram display. We compared each condition by measuring the intensity of the first order diffraction pattern of the fringe generated under the Fresnel condition for the phase hologram. Through this paper, we showed the tend about characteristics of the diffraction efficiency according to object point, reconstruction distance, laser and SLM.

• Smart Media Journal
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• v.8 no.2
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• pp.9-15
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• 2019

Since the computational complexity for hologram generation increases exponentially with respect to the size of the point cloud, parallel processing using CUDA and/or OpenCL library based on multiple GPUs has recently become popular. The CUDA kernel for parallelization needs to consist of threads, blocks, and grids properly in accordance with the number of cores and the memory size in the GPU. In addition, in case of multiple GPU environments, the distribution in grid-by-grid, in block-by-block, or in thread-by-thread is needed according to the number of GPUs. In order to evaluate the performance of CGH generation, we compared the computational speed in CPU, in single GPU, and in multi-GPU environments by gradually increasing the number of points in a point cloud from 10 to 1,000,000. We also present a memory structure design and a calculation method required in the CUDA-based parallel processing to accelerate the CGH (Computer Generated Hologram) generation operation in multiple GPU environments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.444-446
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• 2015

컴퓨터 생성 홀로그램(CGH: computer generated hologram) 기법은 기존의 홀로그램의 광학적 장치의 단점을 보완하여 범용 컴퓨터에서 홀로그램을 생성할 수 있도록 하는 기술이다. CGH는 입력으로 주어지는 물체의 3차원 정보와 출력으로 나오는 디지털 홀로그램의 해상도에 따라 그 연산량이 결정 된다. CGH는 단순하고 반복적인 수학적 계산을 통하여 디지털 홀로그램을 생성하게 되는데, 기존의 연구들에서는 GPU(graphic processing unit)를 이용하여 알고리즘들을 병렬적으로 처리한다. 본 논문에서는 기존연구에서 쓰인 GPU를 이용한 CGH을 개선하여 GPU가 장착되지 않은 상용 컴퓨터에서 GPU가 장착된 다른 컴퓨터들의 연산 자원을 활용하여 CGH를 수행 할 수 있는 프로그램의 개발 방법을 제안 한다. 본 시스템은 GPU가 요구되지 않는 한 개의 서버 컴퓨터와 GPU가 장착된 다수의 클라이언트들로 구성되어 있다. 서버 측에서 물체의 3차원 정보를 입력 받아 각각의 클라이언트들에게 적절한 연산량을 분배하고, 각 클라이언트들은 이미 알려진 GPU 기반 CGH를 통하여 연산을 수행 한 뒤, 그 결과를 서버로 다시 전송하게 된다. 서버는 수신한 각 결과들을 누적하여 입력 받은 물체에 대한 하나의 온전한 홀로그램을 생성할 수 있게 된다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.18-21
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• 2011

디지털 홀로그램은 일반적으로 computer generated hologram(CGH)기법에 의해서 생성된다. 하지만 원리적으로 CGH 기법은 많은 연산량과 복잡도를 요구하고 있기 때문에 실시간으로 디지털 홀로그램을 생성하는 것은 매우 어렵다. 본 논문에서는 CGH 고속연산을 위해 graphics processing unit(GPU)의 병렬처리구조인 CUDA를 사용하였고, 추가적으로 다중 GPU 연산처리를 위해 OpenMP를 사용하였다. 더 나아가 이를 최적화하기 위해서 상수화, 벡터화, 루프풀기 등의 기법들을 제안한다. 결과적으로, 본 논문에서 제안된 기법을 통해서 기존 CPU에서의 CGH 연산속도에 비해 약 8,300배 정도의 속도를 개선할 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.322-324
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• 2011

컴퓨터-생성 홀로그램(computer-generated hologram, CGH) 기법은 실사객체 혹은 가상의 객체로부터 계산에 의해 디지털 홀로그램을 생성해 낼 수 있다. 하지만 HD급 해상도의 디지털 홀로그램 한 프레임을 일반적인 PC를 이용해 계산하기 위해서는 약 10분 정도가 소요된다. 이는 실시간 홀로그래픽 비디오 서비스를 어렵게 하는 문제점 중에 하나이다. 본 논문에서는 CGH 기법의 과도한 연산량을 줄이기 위해 깊이정보(depth-map) 비디오 프레임간의 공간적인 중복성을 이용하는 방법을 제안한다. 이 방법은 인접한 깊이정보 프레임간의 차이를 구해 동일한 깊이값을 갖는 좌표들의 CGH 계산을 생략하는 것이다. 제안한 방법을 적용한 결과 연산속도가 52%정도 향상되는 것을 확인할 수 있었다.