• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.221-224
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• 2010

When the image registration methods which were generally used to the low medium resolution satellite images is applied to the high spatial resolution images, some matching errors or limitations might be occurred because of the local distortions in the images. This paper, therefore, proposed the automatic image-to-image registration of high resolution satellite images using local properties of control points to improve the registration result.

• ETRI Journal
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• 제32권3호
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• pp.390-394
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• 2010

In this paper, we propose a new super-resolution technique based on interpolation of the high-frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The proposed technique uses DWT to decompose an image into different subband images. Then the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new super-resolved image by using inverse DWT. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. For Lena's image, the PSNR is 7.93 dB higher than the bicubic interpolation.

• 한국통신학회논문지
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• 제38C권6호
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• pp.497-502
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• 2013

In this paper, we aim to evaluate the performance of the digital signal processing (DSP) algorithm used in [8] in order to improve the resolution of spectrometers with fixed number of low-cost, non-ideal filters. In such spectrometers, the resolution is limited by the number of filters. We aim to demonstrate via new experiments that the resolution improvement by six times over the conventional limit is possible by using the DSP algorithm as claimed by [8].

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.98-101
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• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.63-66
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• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.272-275
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• 2020

Light Field (LF) cameras capture both spatial and directional information of light rays. Current LF cameras have a problem of a low spatial resolution. There have been lots of existing works carried out to improve the resolution of LF images. In this paper, those existing works will be divided into two categories: hardware-based approaches and software-based approaches, and we will look into and compare several experiment results in order for LF spatial resolution enhancement. Finally, the direction for the future spatial resolution enhancement will be suggested.

• 한국항공우주학회지
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• 제34권6호
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• pp.67-74
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• 2006

본 논문에서는 저가의 클러스터 컴퓨터 시스템과 저해상도 영상장비들을 이용하여 초대형 해석 데이터를 정밀하게 분석할 수 있는 고해상도 타일 가시화 시스템을 개발하였다. 타일 가시화 하드웨어 구축 시 유의점을 고찰하고, 화면왜곡 현상을 제거할 수 있는 빔프로젝터 위치조절장치를 설계/제작하였다. 타일 가시화 소프트웨어 개발에서 그래픽 사용자 인터페이스와 렌더링을 위해서는 Qt와 OpenGL 라이브러리를 이용하였다. 또한 LAM-MPI 라이브러리를 통해 각각의 클러스터 컴퓨터 노드로부터 얻게 되는 조각적인 화면들을 전체의 한 화면으로 동기화시켜 왜곡 없는 전체 타일 영상을 만들도록 하였다.

• Childhood Kidney Diseases
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• 제20권1호
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• pp.18-22
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• 2016

Purpose: The previous reports regarding VUR resolution were not precise due to early frequent surgical intervention. We evaluated the spontaneous resolution (SR) rate and the incidence of new renal scars in primary VUR, focusing on severe reflux. Methods: Medical records of 334 patients with primary VUR who were on medical prophylaxis without surgery for 1 to 9 years, were retrospectively reviewed. Medical prophylaxis was initiated with low-dose antibiotic prophylaxis or probiotics. Radioisotope cystourethrography was performed every 1 to 3 years until SR of reflux. New renal scar was evaluated with follow-up $^{99m}Tc$ DMSA renal scan. Results: The SR rates decreased as VUR grades were getting higher (P=0.00). The overall and annual SR were 58.4% and 14.9%/yr in grade IV reflux and 37.5% and 9.3%/yr in grade V reflux. The median times of SR were 38 months in grade IV reflux and 66 months in grade V reflux. The probable SR rates in grade IV and V reflux were 7.8% and 8.9% in the 1st year, 46.0% and 30.8% in the 3rd year and 74.4% and 64.4% in the 5th year. The incidences of new renal scars between low to moderate reflux and severe reflux showed no significant difference (P=0.32). Conclusion: The SR rates of severe primary VUR were higher than previously reported and most new renal scars were focal and mild.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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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.

• 대한원격탐사학회지
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• 제40권2호
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• pp.141-150
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• 2024

Ground subsidence in urban areas is mainly caused by anthropogenic factors such as excessive groundwater extraction and underground infrastructure development in the subsurface composed of soft materials. Global Navigation Satellite System data with high temporal resolution have been widely used to measure surface displacements accurately. However, these point-based terrestrial measurements with the low spatial resolution are somewhat limited in observing two-dimensional continuous surface displacements over large areas. The synthetic aperture radar interferometry (InSAR) technique can construct relatively high spatial resolution surface displacement information with accuracy ranging from millimeters to centimeters. Although constellation operations of SAR satellites have improved the revisit cycle, the temporal resolution of space-based observations is still low compared to in-situ observations. In this study, we evaluate the extraction of a time-series of surface displacement in Incheon Metropolitan City, South Korea, using the small baseline subset technique implemented using the commercial software, Gamma. For this purpose, 24 COSMO-SkyMed X-band SAR observations were collected from July 12, 2011, to August 27, 2012. The time-series surface displacement results were improved by reducing random phase noise, correcting residual phase due to satellite orbit errors, and mitigating nonlinear atmospheric phase artifacts. The perpendicular baseline of the collected COSMO-SkyMed SAR images was set to approximately 2-300 m. The surface displacement related to the ground subsidence was detected approximately 1 cm annually around a few Incheon Subway Line 2 route stations. The sufficient coherence indicates that the satellite orbit has been precisely managed for the interferometric processing.