• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.197-200
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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 is proved 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 super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.12-18
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• 2006

This paper presents a method to extract a good high-resolution image from a low-resolution video in an automatic manner. Since a high-resolution image reconstruction method utilizing several low-resolution input images works better than a conventional interpolation method utilizing single low-resolution input image only if the input images are well registered onto a common high-resolution grid, low-resolution input images should be carefully chosen so that the registration errors can be carefully considered. In this paper, the statistics obtained from the motion-compensated low-resolution images are utilized to evaluate the feasibility of the input image candidates. Maximum motion-compensation error is estimated from the high-resolution image observation model. U the motion-compensation error of the input image candidate is greater than the estimated maximum motion-compensation error, the input image candidate is discarded. The number of good input image candidates and the statistics of the motion-compensation errors are used to choose final input images. The final input images chosen from the input image selection block are given to the following high-resolution image reconstruction block. It is expected that the proposed method is utilized to extract a good high-resolution image efficiently from a low-resolution video without any user intervention.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.151.2-151.2
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• 2011

IGRINS (Immersion GRating INfrared Spectrometer) will provide the spectra with high-resolution and an instantaneous spectral coverage of H and K band in NIR region. Therefore, it is expected that the wide coverage of wavelength would make a production of an extensive NIR high-resolution spectra of standard stars as a prior program of IGRINS. As a counter part of these NIR spectra, we have planned to obtain the high-resolution spectra of those standard stars in optical band. These optical high-resolution spectra would give us an opportunity to produce the library of high-resolution stellar spectra covering from optical to NIR band, and to confirm the method to determine the stellar parameters and chemical abundances from the NIR high-resolution spectra. Before using the NIR high-resolution spectra, we have tested the method to determine the stellar parameters by comparing between the observed spectra and the synthetic spectra in optical band. In order to make the synthetic spectra, we have used the Kurucz ATLAS9 model grids and the SYNTH code described by Fiorella Castelli (http://wwwuser.oat.ts.astro.it/castelli/). For the cross-check against the parameters that would be derived from the NIR spectra, the stellar parameters such as effective temperature and surface gravity were determined using the optical spectra of the solar-like stars, as preliminary results.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.856-859
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• 2002

A system for high-resolution analysis of defective elementary cell (R, G or B) on Flat Panel Display (FPD) is described. Based on multiple acquisitions of low-resolution shifted images of the display, our system doesn't require a high-resolution sensor neither tedious alignment of the display, and will remain up to date even facing an important increase of the display dimensions. Our process, highly automated and thus flexible and robust, is expected to perform a full analysis in less than 60s. It is mainly intended for production tests and display classification by manufacturers.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.137-139
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• 2008

This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. In this study, an 1m RGB image was generated from 4m IKONOS multispectral data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.659-669
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• 2015

Face Recognition assumes much significance in the context of security based application. Normally, high resolution images offer more details about the image and recognizing a face from a reasonably high resolution image would be easier when compared to recognizing images from very low resolution images. This paper addresses the problem of recognizing faces from a very low resolution image whose size is as low as $8{\times}8$. With the use of CCTV(Closed Circuit Television) and with other surveillance camera-based application for security purposes, the need to overcome the shortcomings with very low resolution images has been on the rise. The present day face recognition algorithms could not provide adequate performance when employed to recognize images from VLR images. Existing methods use super-resolution (SR) methods and Relation Based Super Resolution methods to construct from very low resolution images. This paper uses a learning based super resolution method to extract and construct images from very low resolution images. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.455-457
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• 2009

The purposes of this study are to consider the influence of topography resolution on atmospheric flow simulation and to suggest a method of atmospheric flow simulation using a low-resolution DEM. Simulations using a low-resolution DEM has more critical error at near surface than simulations using high-resolution DEM because it is ignored the small curve topography of high-resolution DEM. Therefore when we convert the height differences between low-resolution DEM and high-resolution DEM into the topography roughness, we can be able to reduce the error on atmospheric flow simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.327-329
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• 2005

Conventional CDDA(Compact Disc Digital Audio) system has limitation which means that bandwidth and resolution of the sign릴 are determined by the sampling frequency and quantization bit, 44.1kHz and 16 bit respectively. Though, new medium such as DVD-audio is developed for high-resolution audio recording, it has high complexity and difficulty in manufacturing process. So, CDDA is a widely used medium for high fidelity audio yet. In this paper, we design a new encoding system for high-resolution audio signal which has backward compatible with conventional CDDA. By evaluating for the encoding and decoding process. we verify the availability of our proposed system.