• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.167-173
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• 2023

X-ray general radiography is the simplest and most important one to get a lot of information. Nevertheless, current x-ray general radiography does not observation in-depth observation. Information about the anatomy of the human body and changes in disease in x-ray general radiography can be obtained but it is difficult to determine the size and shape of the actual lesion due to the disadvantage of expanding the image. In this study, PA and LAT images were acquired and cancer magnification was calculated in the images by measuring the distance of cancer samples. By adjusting the magnification the actual cancer length and thickness were measured and compared with the CT image and the actual cancer sample size. After the PA and LAT images of the inserted 6.0 mm cancer sample were obtained and the magnification was corrected, the length was 5.9 mm and the thickness was 6.1 mm. This value was measured similarly to the actual. The problem of obtaining the magnification that needs to know the actual length from the detector to the cancer sample was secured by obtaining the magnification through PA and LAT images and it is possible to accurately measure the cancer sample size. X-ray general radiography may provide useful information in situations where CT imaging is difficult.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.473-474
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• 2014

본 논문에서는 자연 영상에 존재하는 영상의 곡면 특성을 효과적으로 이용하여 영상을 확대하는 기법을 제안하였다. 제안된 기법이 기존의 기법에 비하여 성능이 향상되었음을 실험을 통하여 확인하였다.

• The Journal of the Korea Contents Association
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• v.7 no.9
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• pp.72-80
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• 2007

Image magnification is the estimation of a few pixel in images with high quality from a pixel of an image with low resolution and there have been studied many techniques to make images with high quality. In this paper, we propose an image interpolation method using median filter and spatial information. The proposed method makes an interpolating pixel using an average value of a median filtered value and an average value of two pixels correlated with an interpolating pixel tightly. Also we make the magnified image with improved quality to add the directional information of surrounding pixels and the characteristic of ones using average value and max value of spatial variation. We evaluate the performance using PSNR in the quality of enlarged image comparing the proposed method with existing methods. The results show the proposed method improves PSNR than the existing methods and make images preserving the characteristic of original imges.

• Annual Conference of KIPS
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• 2024.10a
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• pp.380-383
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• 2024

혀의 동작과 떨림은 건강 상태를 평가하는 중요한 지표로, 혀의 움직임을 정확히 추적하려면 영상분석 기술을 사용하여 물체의 움직임을 추적해야 합니다. 이 연구에서는 표면 특징이 불명확한 혀의 미세한 움직임을 명확히 보기 위해 광류(optical flow) 기술을 사용해 움직임을 확대했습니다. 이를 위해 이미지 확대(upsampling) 알고리즘과 광류 벡터 확대 알고리즘 두 가지 방법을 비교하여, 혀 움직임을 더 정밀하게 추적할 수 있는 방법을 찾고자 했다. 연구 결과, 벡터 확대 알고리즘이 더 정확한 광류를 생성하는 것으로 나타났다. 이는 이미지 확대에서 발생할 수 있는 aliasing 효과를 줄이고, 움직임의 방향과 속도를 더 정확히 표현하기 때문이다. 이러한 방법은 혀뿐만 아니라 초음파 영상을 통한 위장관이나 심장의 움직임을 정밀하게 추적하는 데도 유용할 수 있다. 이 연구는 광류 기반 움직임 분석이 다양한 의료 영상에서 유용하게 사용될 수 있는 가능성을 보여주며, 향후 진단과 치료모니터링에 중요한 도구가 될 수 있음을 시사한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.95-101
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• 2017

In natural images, there is generally locality, and the values of adjacent pixels are similar. It is possible to estimate the curved surface characteristics of the original image using adjacent pixels having similar pixel values. In this paper, after precisely estimating the characteristics of the curved surface existing in the image, interpolation values are obtained so as to faithfully reflect the estimated characteristics of the curved surface, We propose an effective image enlarging method that generates an enlarged image using the obtained interpolation values. The image enlarged by the proposed method maintains the curved surface characteristics of the original image, and thus the image quality of the enlarged image is improved. Experimental results show that the image quality of the proposed method is superior to that of the conventional techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2283-2288
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• 2009

There are a lot of characteristics in Human visual information processing when image information is transmitted from retina to visual cortex. Among them, we analyze the sensibility of the orientation and cortical magnification on an image. The fact that the small fovea is allotted a large area on the cortex is called the cortical magnification factor. We compare recognition rates by weight of vertical, horizontal and diagonal response. In statistics analysis, we show that a particular simple cell responds best to a bar with a vertical orientation. After then, we will apply the characteristics to Human visual system.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.333-338
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• 2004

Today CCTV can be come across easily in public institutions, banks and etc. These CCTV plays very important roles for preventing many kinds of crimes and resolving those crime affairs. But in the case of recording a image of a specific person far from the CCTV, the original image needs to be enlarged and recovered in order to identify the person more obviously. The interpolation is usually used for the enlargement and recovery of the image. This interpolation has a certain limitation. As the magnification of enlargement is getting bigger, the quality of the original image can be worse than before. This paper uses FDP(Face Definition Parameter) of MPEG-4 SNHC FBA group and introduces a new algorithm that the face outline of a face image using Vector Descriptor based on the FDP makes possible better image recovery than the known methods until now.

• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.33-41
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• 2006

Nowadays, CCTV can be come across easily in public institutions, banks, and etc. These CCTV play very important roles for preventing many kinds of crimes and resolving those crime affairs. But in the case of recording image of a specific person far from the CCTV, the original image needs to be enlarged and recovered in order to identify the person more obviously. Interpolation is usually used for the enlargement and recovery of the image in this case. However, it has a certain limitation. As the magnification of enlargement is getting bigger, the quality of the original image can be worse. This paper uses FDP(Facial Definition Parameter) proposed by the MPEG-4 SNHC FBA group and introduces a new algorithm that uses face outline information of the original image based on the FDP, which makes it possible to recover better than the known methods until now.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.73-81
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• 2016

In general, infrared images have less sharpness and image details than visible images. So, the prior image upscaling methods are not effective in the infrared images. In order to solve this problem, this paper proposes an algorithm which initially up-scales an input infrared (IR) image by using adaptive dynamic range encoding (ADRC)-based super-resolution (SR) method, and then fuses the result with the corresponding visible images. The proposed algorithm consists of a up-scaling phase and a fusion phase. First, an input IR image is up-scaled by the proposed ADRC-based SR algorithm. In the dictionary learning stage of this up-scaling phase, so-called 'pre-emphasis' processing is applied to training-purpose high-resolution images, hence better sharpness is achieved. In the following fusion phase, high-frequency information is extracted from the visible image corresponding to the IR image, and it is adaptively weighted according to the complexity of the IR image. Finally, a up-scaled IR image is obtained by adding the processed high-frequency information to the up-scaled IR image. The experimental results show than the proposed algorithm provides better results than the state-of-the-art SR, i.e., anchored neighborhood regression (A+) algorithm. For example, in terms of just noticeable blur (JNB), the proposed algorithm shows higher value by 0.2184 than the A+. Also, the proposed algorithm outperforms the previous works even in terms of subjective visual quality.