• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.836-844
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• 2020

A very large number of pixels is required to generate a computer generated hologram (CGH) with a large-size and wide viewing angle equivalent to that of an analog hologram, which incurs a very large amount of computation. For this reason, a high-performance computing device and long computation time were required to generate high-definition CGH. To solve these problems, in this paper, we propose a technique for generating high-definition CGH by arraying the pre-calculated low-definition CGH and multiplying the appropriately-shifted concave lens function. Using the proposed technique, 0.1 Gigapixel CGH recorded by the point cloud method can be used to calculate 2.5 Gigapixels CGH at a very high speed, and the recorded hologram image was successfully reconstructed through the experiment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7C
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• pp.540-547
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• 2008

In this paper, we proposed a new VLSI architecture which can generate computer-generated hologram (CGH) in real-time and implemented to hardware. The modified algorithm for high-performance CGH was introduced and re-analyzed (or designing hardware. from both numerical and visual analysis, the infernal number system of hardware was decided. CGH algorithm and precision analysis enabled to propose a new cell architecture for CGH. The operational sequence was analyzed with the architecture of CGH cell and the characteristics of the modified CGH algorithm, and finally the pipelined architecture and the operational timing were proposed.

• Korean Journal of Optics and Photonics
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• v.11 no.2
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• pp.80-84
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• 2000

Parabolic almninium mlITOr of m.5('||'&'||'cent; 50 nun) was fabncated by a diamond tummg machine. Computer generated hologram (CGH) for the test of parabolic mirror was encoded by binary phase hologram Approximation of curved fringe to line was made by staircase encoding. After fringe data 1ransformed mto a Post Scnpt file. magnified master CGH was printed by a laser printer, and then it reduced to the photographIc film. Parabolic mirror was tested by Twyman-Green interferometer with CGH at VIewing arm. Its experimental result was compared with those of surface profile and auto-collimatIon test, and then the errors were analyzed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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• pp.1053-1059
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• 2005

The computer generated hologram (CGH) designs and produces digital information for generating 3-D (3-Dimension) image using computer and software instead of optically-sensed hologram of light interference, and it can synthesis a virtual object which is physically not in existence. Since digital hologram includes an amount of data as can be seen at the process of digitization, it is necessary that the data representing digital hologram is reduced for storing, transmission, and processing. As the efforts that are to handle hologram with a type of digital information have been increased, various methods to compress digital hologram called by fringe pattern are groped. Suitable proposal is encoding of hologram. In this paper, we analyzed the properties of CGH using tools of frequency transform, assuming that a generated CGH is a 2D image by introducing DWT that is known as the better tool than DCT for frequency transform. The compression and reconstruction result which was extracted from the wavelet-based codecs illustrates that it has better properties for reconstruction at the maximum 2 times higher compression rate than the Previous researches of Yoshikawa[2] and Thomas[3].

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.129-139
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• 2013

In this paper, we proposed a new hardware architecture for calculating digital holograms at high speed, and verified it with field programmable gate array (FPGA). First, we rearranged memory scheduling and algorithm of computer generated hologram (CGH), and then introduced pipeline technique into CGH process. Finally we proposed a high-performance CGH processor. The hardware was implemented for the target of FPGA, which calculates a unit region of holograms, and it was verified using a hardware environment of NI Inc. and a FPGA of Xilinx Inc. It can generate a hologram of $16{\times}16$ size, and it takes about 4 sec for generating a hologram of a $1,024{\times}1,024$ size, using 6K point sources.

• Current Optics and Photonics
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• v.6 no.1
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• pp.51-59
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• 2022

Holographic display technology is one of the promising 3D display technologies. However, the large amount of computation time required to generate computer-generated holograms (CGH) is a major obstacle to the commercialization of digital hologram. In various systems such as multi-depth head-up-displays with hologram contents, it is important to transmit hologram data in real time. In this paper, we propose a rapid CGH computation method by applying an arraying of a down-scaled hologram with the multiplication of a shifted concave lens function array. Compared to conventional angular spectrum method (ASM) calculation, we achieved about 39 times faster calculation speed for 3840 × 2160 pixel CGH calculation. Through the numerical investigation and experiments, we verified the degradation of reconstructed hologram image quality made by the proposed method is not so much compared to conventional ASM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.435-436
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• 2018

In this paper, diffraction efficiency for computer-generated hologram (CGH) generated under various conditions was measured. This paper discusses the generation conditions that should be considered in hologram reconstruction. We compared each condition by measuring the intensity of the 1st order diffraction pattern of the fringe generated under the Fresnel condition for the phase and complex hologram.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.578-582
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• 2009

Computer-Generated Hologram (CGH) is generally made by Fourier Transform. CGH is made by an optical reconstruction. Computer-Generated Pseudo Hologram (CGPH) is made up Complex Hadamard Transform instead of CGH which is made by the Fourier Transform. CGPH differs from CGH in point of view the possibility of optical reconstruction. There is an advantage that it cannot be optical reconstruction, in other word, physical leakage of the confidential information is impossible. In this paper, a binary image was converted in Complex Hadamard Transform, and CGPH was made. Improvement of the reconstructed image from CGPH is done by error diffusion method and iterative method. The result that the reconstructed image is improved is shown.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1177-1186
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• 2014

Computer-generated hologram (CGH) is to mathematically model optical phenomenon with digital computer. Because it requires huge amount of computational power, a fast and high performance technique is needed. In this paper, we proposed two parallelizations for CGH calculation. The first is to parallelize CGH algorithm in a GPU (general processing unit) and the second is to parallelize multiple GPUs. The proposed algorithm was implemented in GTX780 Ti GPU. It calculates a $1,024{\times}1,024$ hologram with 10K object points for about 24ms.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.54-63
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• 2011

This paper proposes a new equation to calculate computer-generated hologram (CGH) in a high speed and its cell-based VLSI (veri large scale integrated circuit) architecture. After finding the calculational regularity in the horizontal or vertical direction from the basic CGH equation, we induce the new equation to calculate the horizontal or vertical hologram pixel values in parallel. We also propose the architecture of the CGH cell consisting of a initial parameter calculator and update-phase calculator(s) on the basis of the equation and implement them in hardware. Also we show a hardware architecture to parallelize the calculation in the horizontal direction by extending CGH. In the experiments we analyze the used hardware resources. These analyses makes it possible to select the amount of hardware to the precision of the results. Here, for the CGH kernel and the structure of the processor, we used the platform from our previous works.