• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.113-117
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• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.345-354
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method has proven to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper, we applied the super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and are overlapped at high rate. We constructed the observation model between the HR images and LR images applied with the Maximum A Posteriori(MAP) reconstruction method which is one of the major methods in the super resolution grid construction. Based on the MAP method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.543-548
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• 2021

The higher the resolution of the image, the higher the satisfaction of the viewers of the image, and the super-resolution imaging has a considerable increase in research value among the fields of computer vision and image processing. In this study, the main features of low-resolution image LR are extracted mainly using deep learning super-resolution models. It learns and reconstructs the extracted features, and focuses on reconstruction-based algorithms that generate high-resolution image HR. In this paper, we investigate SRCNN and VDSR in a super-resolution algorithm model based on reconstruction. The structure and algorithm process of the SRCNN and VDSR model are briefly introduced, and the multi-channel and special form are also examined in the improved performance evaluation function, and understand the performance of each algorithm through experiments. In the experiment, an experiment was performed to compare the results of the SRCNN and VDSR models with the peak signal-to-noise ratio and image structure similarity, so that the results can be easily judged.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.590-599
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• 2017

In South Korea, sewage pipeline exploration devices have been developed using high resolution digital cameras of 2 mega-pixels or more. On the other hand, most devices are less than 300 kilo-pixels. Moreover, because 100 kilo-pixels devices are used widely, the environment for image processing is very poor. In this study, very low resolution ($240{\times}320$ = 76,800 pixels) images were adapted when it is difficult to detect cracks. Considering that the images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique was studied using digital image processing technology for low resolution images of sewage pipelines. The authors developed a program to automatically detect cracks as 6 steps based on the MATLAB functions. In this study, the second step covers an algorithm developed to find the optimal threshold value, and the fifth step deals with an algorithm to determine cracks. In step 2, Otsu's threshold for images with a white caption was higher than that for an image without caption. Therefore, the optimal threshold was found by decreasing the Otsu threshold by 0.01 from the beginning. Step 5 presents an algorithm that detects cracks by judging that the length is 10 mm (40 pixels) or more and the width is 1 mm (4 pixels) or more. As a result, the crack detection performance was good despite the very low-resolution images.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.87-98
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• 2000

Image merging techniques have been developed to integrate the advantage of different data type. The objective of this study is to present the optimal method for merging high spatial resolution panchromatic image, such as the latest commercial satellite data, and low spatial resolution mulitspectral images. For this study, a set of 2m resolution panchromatic and 8m resolution mulitspectral data were simulated by using airborne mulitspectral data. Five merging methods of MWD, IHS, PCA, HPF, and CN were applied to produce four bands of high spatial resolution mulitspectral data. Merging results were evaluated by visual interpretation, image statistics, semivariogram, and spectral characteristics. From the aspects of both spatial resolution and spectral information, the wavelet-based MWD merging method have shown very similar results compared with the original data used for the merging.

• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.236-244
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• 2011

Recently, the number of interventional procedures has increased dramatically as an alternative of invasive surgical procedure. The need for the quality control program of the angiographic units has also increased, because of concerns about the increased patient dose and the importance of image quality of angiographic units for the successful procedures. The purpose of this study was to propose an optimal guideline for the quality control program of the angiographic units. We reviewed domestic and international standards about medical imaging system and we evaluated the quality of 61 angiographic units in Korea with the use of NEMA 21 phantom. According to the results of our study, we propose a guideline for the quality control program of the angiographic units. Quality control program includes tube voltage test, tube current test, HVL test, image-field geometry test, spatial resolution test, low-contrast iodine detectability test, wire resolution test, phantom entrance dose test. Proposed reference levels are as follows: PAE < $\pm$ 10% in tube voltage test, PAE < $\pm$ 15% in tube current test, minimum 2.3 mmAl at 80 kVp in HVL test, minimum 'acceptable' level at image-field geometry test, 0.8 lp/mm for detector size of 34-40cm, 1.0 lp/mm for detector size of 28-33cm, 1.2 lp/mm for detector size of 22-27cm in spatial resolution test, minimum 200mg/cc in low contrast iodine detectability test, phantom entrance dose should be under 10R/min, 0.012 inch wire should be seen at static wire resolution test, and 0.022 inch wire should be seen at moving wire resolution test.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.724-726
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• 2003

Currently available image fusion methods are not efficient for fusing the Landsat 7 images. Significant color distortion is one of the major problems. In this paper, using the well-known wavelet based method for data fusion between high-resolution panchromatic and low-resolution multispectral satellite images, we performed Landsat 7 image fusion. Based on the experimental results obtained from this study, we analyzed some reasons for color distortion. A new approach using the biorthogonal wavelets based method for data fusion is presented. This new method has reached an optimum fusion result - with the same spectral resolution as the multispectral image and the same spatial resolution as the panchromatic image with minimum artifacts.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.50-59
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• 1996

In this paper, a new hierarchical motion estiamtion scheme using the wavelet transformed multi-resolution image layers is proposed. Compared with the full search motion estimation method, the existing hierarchical methods remarkably reduce the amount of the computation but their efficiencies are depreciated by the local minima problem. In order to solve the local minima problem, the multi-resolution image layers are composed using the wavelet transform and the number of layers participated in the motion estimation for a block is determined by considering of its low band energy and higher band energy on the first wavelet transformed layer. The ratio between higher band energy and low band energy of each block is evaluated and in the case of the blocks which include relatively large higher band energy, the motion estimation is carried out in the high resolution layer. Otherwise, all layers are used. The final motion vectors are obtained in the first wavelet transformed layer. So less bits for motion vectors are transmitted, and the decomposition of received image using inverse wavelet transform decreases the blocking effect.