• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.540-541
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• 2018

In this paper, we propose an super-resolution method that improves resolution by using DWT (Discrete Wavelet Transform) for low resolution infra-red images. In this method, DWT is performed in a manner that does not reduce the resolution of an image input through an infra-red camera to generate sub-bands of the same resolution (LH, HL, and HH) And the original infra-red image is used to perform an inverse-DWT to obtain an infra-red image with improved resolution. Experimental results show that the mean SSIM value of the proposed method is 0.989861, which is about 0.004 higher than that of the conventional Bi-linear and Bi-cubic filters.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.68-71
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• 2020

최근 딥러닝을 이용한 인체 자세 추정(human pose estimation) 연구가 활발히 진행되고 있다. 그 중 구조가 간단하면서도 성능이 강력하여 널리 사용되고 있는 딥러닝 네트워크 모델은 이미지 분류(image classification)에 사용되는 백본 네트워크(backbone network)와 디컨볼루션 출력망(deconvolution head network)을 이어 붙인 구조를 갖는다[1]. 기존의 디컨볼루션 출력망은 디컨볼루션 층을 쌓아 낮은 해상도의 특징맵을 모두 높은 해상도로 변환한 후 최종 인체 자세 추정을 하는데 이는 다양한 해상도에서 얻어낸 특징들을 골고루 활용하기 힘들다는 단점이 있다. 따라서 본 논문에서는 매 디컨볼루션 층 이후에 인체 자세 추정을 하여 다양한 해상도에서 연산을 하고 이를 종합하여 최종 인체 자세 추정을 하는 방법을 제안한다. 실험 결과 Res50 과 기존의 디컨볼루션 출력망의 경우 0.717 AP 를 얻었는데 Res101 과 기존의 디컨볼루션 출력망을 사용한 결과 50% 이상의 파라미터 수 증가와 함께 0.727 AP, 즉 0.010AP 의 성능 향상이 이루어졌다. 이에 반해 Res50 에 다중 해상도 디컨볼루션 출력망을 사용한 결과 약 1%의 파라미터 수 증가 만으로 0.720 AP, 즉 0.003 AP 의 성능 향상이 이루어졌다. 이를 통해 디컨볼루션 출력망 구조를 개선하면 매우 적은 파라미터 수 증가 만으로도 인체 자세 추정의 성능을 효과적으로 향상시킬 수 있음을 확인하였다.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.18-26
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• 2009

Image resolution enhancement algorithm is a basic technique for image enlargement and restoration. The main problem is the image quality degradation such as blurring or blocking effects. In this paper, we propose loss information estimation and image resolution enhancement method using low level interpolation method. In the proposed method, loss information is computed by downsampling -interpolation process of obtained low resolution image. We estimate loss information of high resolution image using interpolation of the computed loss information. Lastly, we add up interpolated high resolution image and the estimated loss information which is applied a weight factor. Our experiments obtained the average PSNR 1.4dB which is improved results better than conventional algorithm. Also subjective image quality is more clearness and distinctness. The proposed method may be helpful for various video applications which required improvement of image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2664-2669
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• 2011

We proposed a new method to improve the resolution of far object image in curving effective integral imaging system. Basically, the curving effective integral imaging(CEII) system can improve the resolution of the reconstructed images with an increased sampling rate of elemental images. However, in the case when an object located far from the lenslet array is picked up, the low resolution of the reconstructed images of the far object has been a primary problem because the sampling rate is very low. In order to solve this drawback, by using the direct pixel mapping(DPM) method the EIA picked up from a far object is transformed into a new EIA that virtually looks like the EIA picked up from the object originally located close to the lenslet array. From this new EIA, highly resolution-enhanced images of far object could be reconstructed in the CEII system. To show the feasibility of the proposed method, simulation results are compared with the conventional method.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.121-129
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• 2021

This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.118-128
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• 2015

Compressive sensing (CS) has to face with two challenges of computational complexity reconstruction and low coding efficiency. As a solution, this paper presents a novel spatially scalable Kronecker two layer compressive sensing framework which facilitates reconstruction up to three spatial resolutions as well as much improved CS coding performance. We propose a dual-resolution sensing matrix based on the quincunx sampling grid which is applied to the base layer. This sensing matrix can provide a fast-preview of low resolution image at encoder side which is utilized for predictive coding. The enhancement layer is encoded as the residual measurement between the acquired measurement and predicted measurement data. The low resolution reconstruction is obtained from the base layer only while the high resolution image is jointly reconstructed using both two layers. Experimental results validate that the proposed scheme outperforms both conventional single layer and previous multi-resolution schemes especially at high bitrate like 2.0 bpp by 5.75dB and 5.05dB PSNR gain on average, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.275-281
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• 2019

A synthetic aperture radar(SAR) satellite is more effective than an optical satellite for target analysis because an SAR satellite can provide two-dimensional electromagnetic scattering distribution of a target during all-weather and day-and-night operations. To conduct target analysis while considering the earth observation interval of an SAR satellite, observing a specific area as wide as possible would be advantageous. However, wider the observation area, worse is the resolution of the associated SAR satellite image. Although conventional methods for improving the resolution of radar images can be employed for addressing this issue, few studies have been conducted for improving the resolution of SAR satellite images and analyzing the performance. Hence, in this study, the applicability of conventional methods to SAR satellite images is investigated. SAR target detection was first applied to Korea Multipurpose Satellite-5(KOMPSAT-5) SAR images provided by Korea Aerospace Research Institute for extracting target responses. Extrapolation, RELAX, and MUSIC algorithms were subsequently applied to the target responses for improving the resolution, and the corresponding performance was thereby analyzed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.3
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• pp.263-269
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• 2000

As the accuracy of digital photogrammetry is restricted by the resolution of image to be used, it is axiomatic that the resolution of image should be improved. As for the method to constitute hardware with CCD sensor that capacity was expanded or the method to acquire the image of high resolution by deciding the quantity of sub-pixel in advance through moving sensor, the price is expensive. This study tries to enhance the resolution of low resolution image by acquiring the image with the digital camera that the price is cheap and deciding shifts and rotations through matching multiple digital image by means of least square method. As the result of study, the resolution of digital image was improved greatly. So, not only the digital photogrammetry which has the competitive power of price economically is possible in the future but also the application is expected widely.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.191-193
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• 2015

우리나라 산업단지의 수출경쟁력은 중국 등 주변 국가들의 원가에 상대적으로 취약하다. 수출경쟁력 차원에서 원가를 구성하는 항목들은 여러가지가 있겠으나, 원가절감이 가능한 항목은 그리 많지 않다. 본 연구에서는 국내 수출기업들의 수출경쟁력 향상을 위해 해상물류비용의 절감이 가능한 방안을 탐색하여 연구하였다. 특히 해외에서 소금과 같은 살화물을 수입하고 공선으로 나가 다시 수입하는 벌크선의 경우에는 국내에서 공선상태로 나가는 선박을 대상으로 컨테이너 적재가 가능한 겸용선으로 개조하여 운항시에 컨테이너로 수출하는 해상물류비용을 낮출 수 있다. 따라서 산업단지의 수출기업들이 이를 활용하면 해상물류비용을 획기적으로 낮출수 있으므로 수출경쟁력을 향상시킬수 있고, 이러한 절감방안 모델은 정부, 항만운영사업자, 수출기업 및 하역사업자 모두에게 도움이 되는 모델로 해상물류비용을 절감하기 위해 충분한 연구 가치가 있다.