• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.10 s.352
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• pp.22-27
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• 2006

In this paper, we propose a method that can generate a computer-generated hologram (CGH) from the depth stream and color image outputs provided by an active sensor add-on camera. Distinguished from an existing holographic display system that uses a computer graphic model to generate CGH, this method utilizes a real camera image including a depth information for each object captured by the camera, as well as color information. This procedure consists of two steps that the acquirement of a depth-annotated image of real object, and generation of CGH according to the 3D information that is extracted from the depth cue. In addition, we display the generated CGH via a holographic display system. In experimental system we reconstruct an image made from CGH with a reflective LCD panel that had a pixel-pitch of 10.4um and resolution of 1408X1050.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.497-502
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• 2001

We propose a method to represent binary data by modulating both the intensity and the phase of uniform plane waves with a twisted-nematic liquid crystal display for holographic digital data storage especially in a disk-shaped recording medium. As far as intensity modulation is concerned, our method is not different from the conventional method, because binary bit values 0 and 1 are represented by the dark (off) and lit (on) states of the liquid crystal display pixels, respectively (or vice versa). With our method, however, the on pixels are also controlled so that the beams passing through them can have one of two different phase delays. If the difference of the two phase delays is close to 180 degrees, we can reduce the dc component of the data image and thus improve the beam intensity uniformity at the holographic recording plane when Fourier plane holograms are recorded. The feasibility of our method is experimentally demonstrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.353-357
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• 2005

본 논문에서는 영상 입력 장치 또는 카메라 이미지 센서로부터 얻은 Bayer Data입력 포맷을 우리가 디스플레이 장치로 보는 영상으로 출력하기 위해 전처리 작업을 수행한다. 먼저 들어오는 Bayer Data Format은 인터폴레이션을 수행하여 컬러영상을 표현하기위한 한 픽셀 표현 R, G, B값을 구한다. 본 논문에서는 연산량과 필요한 레지스터의 수를 줄이고 칩의 성능을 향상시키기 위해 기존 3${\times}$3라인 쓰지 않고 2${\times}$2라인을 이용한 인터폴레이션을 수행한다. 또한 Bayer Data입력에 대한 이미지 스케일링 작업과 인터폴레이션 수행 작업을 동시에 수행한다. 이를 구현하기위해 원본 이미지 사이즈를 640${\times}$480으로 입력 데이터를 사용하고, 소프트웨어로 전처리하여 이미지 결과를 확인한 후, 최적화된 알고리즘를 적용하여 VHDL설계언어를 이용한 하드웨어 설계후, ModelSim 6.0a를 이용하여 데이터를 검증한다.

• Annual Conference of KIPS
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• 2003.05a
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• pp.631-634
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• 2003

오늘날 CT나 MR등을 통한 의학 영상 기술과 컴퓨터 성능의 향상으로 인체 내부 장기의 영상을 비교적 용이하게 얻을 수 있으며 얻어진 영상 정보는 컴퓨터로 수치화 되므로 데이터의 조작 및 가공이 용이하다. 그러나, 이렇게 얻어진 의학 영상들은 보통 2 차원적 슬라이스 image 형태로 얻어진다. 일반적으로 슬라이스 사이의 간격은 조사량 등 여러 문제 때문에, 항상 동일한 간격을 유지하고 있지 않은 경우가 많으며 슬라이스 사이 간격이 슬라이스 내의 픽셀 간격보다 큰 경우가 대부분이다. 이러한 image로부터 3 차원적 디스플레이나, 조작, 분석을 하기 위해서는 같은 간격의 image를 얻어야 한다. 이러한 이유로 인하여 보간(Interpolation) 기법이 의학 영상 분야에서 많이 사용된다. 본 논문은 형태-기반 보간 방법을 gray-scale image 에 적용이 가능하도록 확장한 그레이 수준 형태 기반 보간 알고리즘을 구현하였다. 그리하여, 본 논문이 제안한 알고리즘을 슬라이스 간격이 큰 2차원 복부 CT 영상에 적용시켜 다른 보간 법들보다 향상된 결과를 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.123-131
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• 2009

The emerging display technology has been replaced the previous position of the CRT with the LCD. The nature of hold type display such as LCD, however, causes many problems such as motion blur and motion judder. To resolve the problems, we used frame interpolation technique which improves the image quality by inserting new interpolated frames between existing frames. In this paper, we propose a novel frame interpolation technique that uses dominant MV and variance different value in each block. At first, the proposed algorithm performs unidirectional motion estimation using blocking matching algorithm. The new frame is generated by pixel average using compared block variance or by pixel motion compensation using dominant motion vector, whether the motion estimation find the target area or not. Several experiments with the proposed algorithm shows that the proposed algorithm has better image quality than the existing bidirectional frame interpolation algorithm at the rate of about 3dB PSNR and has low complexity comparing to the unidirectional frame interpolation technique.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.60-69
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• 2007

The human eye usually experiences a loss of color sensitivity when it is subjected to high levels of luminance, and perceives a discrepancy in color between high and normal-luminance displays, generally known as a hue shift. Accordingly, this paper models the hue-shift phenomenon and proposes a hue-correction method to provide perceptual matching between high and normal-luminance displays. The value of hue-shift is determined by perceived hue matching experiments. At first the phenomenon is observed at three lightness levels, that is, the ratio of luminance is the same between high and normal-luminance display when the perceived hue matching experiments we performed. To quantify the hue-shift phenomenon for the whole hue angle, color patches with the same lightness are first created and equally spaced inside the hue angle. These patches are then displayed one-by-one on both displays with the ratio of luminance between two displays. Next, the hue value for each patch appearing on the high-luminance display is adjusted by observers until the perceived hue for the patches on both displays appears the same visually. After obtaining the hue-shift values, these values are fit piecewise to allow shifted-hue amounts to be approximately determined for arbitrary hue values of pixels in a high-luminance display and then used for correction. Essentially, input RGB values of an image is converted to CIELAB values, and then, LCh (lightness, chroma, and hue) values are calculated to obtain the hue values for all the pixels. These hue values are shifted according to the amount calculated by the functions of the hue-shift model. Finally, the corrected CIELAB values are calculated from corrected hue values, after that, output RGB values for all pixels are estimated. For evaluation, an observer's preference test was performed with hue-shift results and Almost observers conclude that the images from hue-shift model were visually matched with images on normal luminance display.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.137-144
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• 2014

In this paper, we propose the histogram matching-based power reduction technique considering the perceptual image quality. The conventional methods cannot analyze the clipping error of an image, and hence, they significantly degrade the image quality when pixels with the clipping error are concentrated on small area. The proposed method generates histograms for various images with different characteristics, and it calculates and stores the optimal clipping rate in a database. Then, it compares the histograms with that of an input image, and selects the histogram and clipping rate with the minimum difference to prevent the image quality degradation. In the experimental results, the proposed method improved the average PSNR and SSIM by up to 15.795 dB and 0.036, compared with the conventional methods.

• Journal of KIISE
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• v.43 no.11
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• pp.1298-1306
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• 2016

Backlight has a major factor in power consumption of TFT-LCDs which are most popular in portable devices. There have been a lot of attempts to achieve power savings by backlight dimming. At the same time, the researches have shown image compensation due to decreased brightness of a displayed image. However, existing image compensation methods such as histogram equalization have some limits in completely satisfying the human visual system (HVS)-awareness. This paper proposes an enhanced dimming technique to obtain both power saving and HVS-awareness by combining pixel compensation and histogram specification for TFT-LCDs. This method executes a search algorithm and an automation algorithm employing simplified calculations for fast image processing. Experimental results showed that the proposed method achieved significant improvement in visual satisfaction per power saving over existing backlight dimming.