• Korean Journal of Radiology
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• v.19 no.6
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• pp.1031-1041
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• 2018

Objective: To compare image quality and radiation dose of high-pitch dual-source spiral cardiothoracic computed tomography (CT) between non-electrocardiography (ECG)-synchronized and prospectively ECG-triggered data acquisitions in young children with congenital heart disease. Materials and Methods: Eighty-six children (${\leq}3$ years) with congenital heart disease who underwent high-pitch dual-source spiral cardiothoracic CT were included in this retrospective study. They were divided into two groups (n = 43 for each; group 1 with non-ECG-synchronization and group 2 with prospective ECG triggering). Patient-related parameters, radiation dose, and image quality were compared between the two groups. Results: There were no significant differences in patient-related parameters including age, cross-sectional area, body density, and water-equivalent area between the two groups (p > 0.05). Regarding radiation dose parameters, only volume CT dose index values were significantly different between group 1 ($1.13{\pm}0.09mGy$) and group 2 ($1.07{\pm}0.12mGy$, p < 0.02). Among image quality parameters, significantly higher image noise ($3.8{\pm}0.7$ Hounsfield units [HU] vs. $3.3{\pm}0.6HU$, p < 0.001), significantly lower signal-to-noise ratio ($105.0{\pm}28.9$ vs. $134.1{\pm}44.4$, p = 0.001) and contrast-to-noise ratio ($84.5{\pm}27.2$ vs. $110.1{\pm}43.2$, p = 0.002), and significantly less diaphragm motion artifacts ($3.8{\pm}0.5$ vs. $3.7{\pm}0.4$, p < 0.04) were found in group 1 compared with group 2. Image quality grades of cardiac structures, coronary arteries, ascending aorta, pulmonary trunk, lung markings, and chest wall showed no significant difference between groups (p > 0.05). Conclusion: In high-pitch dual-source spiral pediatric cardiothoracic CT, additional ECG triggering does not substantially reduce motion artifacts in young children with congenital heart disease.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.205-208
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• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.1-6
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• 2009

It is very difficult to restore the images degraded by motion blur and additive noise. In conventional methods, regularization usually applies to all the images without considering local characteristics of the images. As a result, ringing artifacts appear in the edge regions and noise amplification is in the flat regions, as well. To solve these problems, we propose an adaptive iterative regularization method, using the way of regularization operator considering edge directions. In addition, we suggest an adaptive regularization parameter and an relaxation parameter. In conclusion, We have verified that the new method shows the suppression of the noise amplification in the flat regions, also does less ringing artifacts in the edge regions. Furthermore, it offers better images and improves the quality of ISNR, comparing with those of conventional methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3928-3934
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• 2010

To restore image degraded by motion blur and additive noise, In conventional method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive regularization iterative restoration using wavelet directional considering edges and the regularization operator with no direction for flat regions. We verified that the proposed method showed results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.1049-1052
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• 2004

In general, contrary to the CRTs with impulsive emission, liquid crystal displays have motion artifacts such as blurring. ghost image, decrease of dynamic CR(contrast ratio), and stroboscopic motion due to hold type driving method. In this paper, to improve motion picture quality of LCDs. impulsive driving method of black data insertion was applied to the OCB mode which is well known for its fast LC response time and wide viewing angle properties. Subject evaluation was carried out with CRT, TN, SIPS(Super IPS). and impulsive driving OCB. Moving picture image quality near CRT was obtained in impulsive OCB driving mode

• ETRI Journal
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• v.31 no.1
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• pp.1-9
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• 2009

Frequency domain analysis is a fundamental procedure for understanding the characteristics of visual data. Several studies have been conducted with 2D videos, but analysis of stereoscopic 3D videos is rarely carried out. In this paper, we derive the Fourier transform of a simplified 3D video signal and analyze how a 3D video is influenced by disparity and motion in terms of temporal aliasing. It is already known that object motion affects temporal frequency characteristics of a time-varying image sequence. In our analysis, we show that a 3D video is influenced not only by motion but also by disparity. Based on this conclusion, we present a temporal anti-aliasing filter for a 3D video. Since the human process of depth perception mainly determines the quality of a reproduced 3D image, 2D image processing techniques are not directly applicable to 3D images. The analysis presented in this paper will be useful for reducing undesirable visual artifacts in 3D video as well as for assisting the development of relevant technologies.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.41-49
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• 2008

H.264/AVC supports variable block motion compensation, multiple reference images, 1/4-pixel motion vector accuracy, and in-loop deblocking filter, compared with the existing compression technologies. While these coding technologies are major functions of compression rate improvement, they lead to high complexity at the same time. For the H.264 video coding technology to be actually applied on low-end / low-bit rates terminals more extensively, it is essential to improve tile coding speed. Currently the deblocking filter that can improve the moving picture's subjective image quality to a certain degree is used on low-end terminals to a limited extent due to computational complexity. In this paper, a performance improvement method of the deblocking filter that efficiently reduces the blocking artifacts occurred during the compression of low-bit rates digital motion pictures is suggested. In the method proposed in this paper, the image's spatial correlational characteristics are extracted by using the variable block information of motion compensation; the filtering is divided into 4 modes according to the characteristics, and adaptive filtering is executed in the divided regions. The proposed deblocking method reduces the blocking artifacts, prevents excessive blurring effects, and improves the performance about $30{\sim}40%$ compared with the existing method.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.9-14
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• 2021

Recently, magnetic resonance imaging (MRI), which can acquire images with good contrast without exposure to radiation, has been widely used for diagnosis. However, noise that reduces the accuracy of diagnosis is essentially generated when acquiring the MR images, and by adjusting the parameters, the noise problem can be solved to obtain an image with excellent characteristics. Among the parameters, the number of excitation (NEX) can acquire images with excellent characteristics without additional degradation of image characteristics. In contrast, appropriate NEX setting is required since the scan time increases and motion artifacts may occur. Therefore, in this study, after fixing all MRI parameters through the MRiLab simulation program, we tried to evaluate the tendency of image characteristics according to changing NEX through quantitative evaluation of brain T2 weighted images acquired by adjusting only NEX. To evaluate the noise level and similarity of the acquired image, signal to noise ratio (SNR), contrast to noise ratio (CNR), root mean square error (RMSE) and peak signal to noise ratio (PSNR) were calculated. As a result, both noise level and similarity evaluation factors showed improved values as NEX increased, while the increasing width gradually decreased. In conclusion, we demonstrated that an appropriate NEX setting is important because an excessively large NEX does not affect image characteristics improvement and cause motion artifacts due to a long scan.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.149-159
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• 2001

The conventional block-based motion prediction suffers, especially in low bit-rate video application, from shortcomings such as blocking artifacts of motion field and unstable motion estimation. To overcome the deficiency, this paper proposes one method of adopting a new motion compensation scheme based on the irregular triangular mesh structure while keeping the current block-based DCT coding structure of H.263 as much as possible. To represent the reconstructed previous frame using minimal number of control points, the proposed method designs content-adaptive irregular triangular meshes, and then, estimate the motion vector of each control point using the affine transformation-based matching. The predicted current frame is obtained by applying the affine transformation to each triangular mesh. Experiment with the several real video sequences shows improvement both in objective and subjective picture quality over the conventional block-based H.263 method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.940-947
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for high quality display. However, MCFI that directly uses the motion information often suffers from annoying artifacts such as blockiness, ghost effects, and deformed structures. So in this paper, we propose a novel edge-based adaptively weighted vector median filter as post-processing. At first, the proposed method generates an edge direction map through a sobel mask and a weighted maximum frequent filter. And then, outlier MVs are removed by average of angle difference and replaced by a median MV of $3{\times}3$ window. Finally, weighted vector median filter adjusts the weighting values based on edge direction derived from spatial coherence between the edge direction continuity and motion vector. The results show that the performance of PSNR and SSIM are higher up to 0.5 ~ 1 dB and 0.4 ~ 0.8 %, respectively.