• 전자공학회논문지B
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• 제33B권8호
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• pp.81-95
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• 1996

In this paper, we propose a motio estiamtion algorithm using low-resolution quantization to reduce the computation of the full search algorithm. The proposed algorithm consists of the low-resolution search which determins the candidate motion vectors by comparing the low-resolution image and the full-resolution search which determines the motion vector by comparing the full-resolution image on the positions of the candidate motion vectors. The low-resolution image is generated by subtracting each pixel value in the reference block or the search window by the mean of the reference block, and by quantizing it is 2-bit resolution. The candidate motion vectors are determined by counting the number of pixels in the reference block whose quantized codes are unmatched to those in the search window. Simulation results show that the required computational cost of the proposed algorithm is reduced to 1/12 of the full search algorithm while its performance degradation is 0.03~0.12 dB.

• 스마트미디어저널
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• 제8권4호
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• pp.64-71
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• 2019

The performance of an OMR (Optical Music Recognition) system is usually determined by the characterizing features of the input music score images. Low resolution is one of the main factors leading to degraded image quality. In this paper, we handle the low-resolution problem using the super-resolution technique. We propose the use of a deep neural network with instance normalization to improve the quality of music score images. We apply instance normalization which has proven to be beneficial in single image enhancement. It works better than batch normalization, which shows the effectiveness of shifting the mean and variance of deep features at the instance level. The proposed method provides an end-to-end mapping technique between the high and low-resolution images respectively. New images are then created, in which the resolution is four times higher than the resolution of the original images. Our model has been evaluated with the dataset "DeepScores" and shows that it outperforms other existing methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.648-650
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• 2003

Efficient multi-resolution image fusion aims to take advantage of the high spectral resolution of Landsat TM images and high spatial resolution of SPOT panchromatic images simultaneously. This paper presents a multi-resolution data fusion scheme, based on multirate image representation. Motivated by analytical results obtained from high-resolution multispectral image data analysis: the energy packing the spectral features are distributed in the lower frequency bands, and the spatial features, edges, are distributed in the higher frequency bands. This allows to spatially enhancing the multispectral images, by adding the high-resolution spatial features to them, by a multirate filtering procedure. The proposed method is compared with some conventional methods. Results show it preserves more spectral features with less spatial distortion.

• Current Optics and Photonics
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• 제1권1호
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• pp.34-44
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• 2017

Stereoscopic (S3D) displays present different images to the two eyes. Temporal multiplexing and spatial multiplexing are two common techniques for accomplishing this. We compared the effective resolution provided by these two techniques. In a psychophysical experiment, we measured resolution at various viewing distances on a display employing temporal multiplexing, and on another display employing spatial multiplexing. In another experiment, we simulated the two multiplexing techniques on one display and again measured resolution. The results show that temporal multiplexing provides greater effective resolution than spatial multiplexing at short and medium viewing distances, and that the two techniques provide similar resolution at long viewing distance. Importantly, we observed a significant difference in resolution at the viewing distance that is generally recommended for high-definition television.

• Nuclear Engineering and Technology
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• 제53권4호
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• pp.1311-1317
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• 2021

In this study, an airborne alpha detection system, which consists of a passivated implanted planar silicon (PIPS) detector and an air filter, was developed. A collimator applied to the alpha detection system showed an enhancement in resolution and a degradation in detection efficiency. The resolution and detection efficiency were compared and analyzed to evaluate the performance of the collimator. Thus, the resolution was found to be more important than the efficiency as a determining factor of the detection system performance, from the viewpoint of radionuclide identification. The performance was evaluated on three properties of the collimator: hole shape, hole length, and the ratio between the hole and frame pitches. From the hole shape performance evaluation, a hexagonal collimator showed the highest resolution. Further, the collimator with a hole pitch of 14 mm was found to have the highest resolution while that with a frame pitch of 4-6 mm (i.e., 1.2-1.4 times longer than the hole pitch) showed the highest resolution.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.71-76
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• 2020

In this paper, we propose a super-resolution method that reconstructs compressed low-resolution images into high-resolution images. We propose a CNN model with a small number of parameters, and even if quantization is applied to the proposed model, super-resolution can be implemented without deteriorating the image quality. To further improve the quality of the compressed low-resolution image, a new degradation model was proposed instead of the existing bicubic degradation model. The proposed degradation model is used only in the training process and can be applied by changing only the parameter values to the original CNN model. In the super-resolution image applying the proposed degradation model, visual artifacts caused by image compression were effectively removed. As a result, our proposed method generates higher PSNR values at compressed images and shows better visual quality, compared to conventional CNN-based SR methods.

• 한국수자원학회논문집
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• 제56권10호
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• pp.641-653
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• 2023

기후변화와 도시화의 영향으로 인해 자연재해의 발생빈도와 규모가 증가하고 있다. 특히 도시 침수는 발생 시간이 짧고 막대한 인명 및 경제적 손실을 초래할 수 있기 때문에 신속하고 정확도 높은 예측 정보 생산이 중요하다. 하지만, 기존 물리과정 및 인공지능 기반 기법은 고해상도 침수 해석을 위해 많은 전산 자원이나 데이터가 요구되는 한계가 있다. 본 연구에서는 딥러닝 기반 초해상화(Super-Resolution) 기법을 통한 고해상도 도시 침수 해석 방법을 제안하고 적용성을 평가한다. 제안된 방법은 고해상도 물리 모형의 결과로 훈련된 초해상화 딥러닝 모형을 이용하여 저해상도 침수 해석 이미지를 고해상도로 변환한다. 미국 포틀랜드 도심지의 두 가지 침수 사례에 대해 적용, 4 m 공간해상도 물리 모의 결과를 1 m 급 고해상도 침수 해석 정보로 초해상화 하였으며, 초해상화 이미지와 고해상도 원본 간 높은 구조적 유사성이 확인되었다. 성능 지표로 평가한 결과, 전체 검증 대상 이미지에 대한 평균 PSNR 22.77 dB, SSIM 0.77로 우수하여, 초해상화 기법의 도시 침수 해석 적용성이 검증되었다. 제안된 방법은 적은 양의 침수 시나리오만으로도 효율적인 딥러닝 모형 훈련이 가능하고, 물리 모형의 정보를 최대한 활용할 수 있기 때문에, 고해상도 도시 침수 정보 생산에 효과적으로 사용될 수 있을 것으로 기대된다.

• 대기
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• 제18권4호
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• pp.387-395
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• 2008

The impact of horizontal resolution on a regional climate model was investigated by simulating precipitation over the Korean Peninsula. As a regional climate model, the SNURCM(Seoul National University Regional Climate Model) has 21 sigma layers and includes the NCAR CLM(National Center for Atmospheric Research Community Land Model) for land-surface model, the Grell scheme for cumulus convection, the Simple Ice scheme for explicit moisture, and the MRF(Medium-Range Forecast) scheme for PBL(Planetary Boundary Layer) processing. The SNURCM was performed with 20 km resolution for Korea and 60 km resolution for East Asia during a 20-year period (1980-1999). Although the SNURCM systematically underestimated precipitation over the Korean Peninsula, the increase of model resolution simulated more precipitation in the southern region of the Korean Peninsula, and a more accurate distribution of precipitation by reflecting the effect of topography. The increase of precipitation was produced by more detailed terrain data which has a 10 minute terrain in the 20 km resolution model compared to the 30 minute terrain in the 60 km resolution model. The increase in model resolution and more detailed terrain data played an important role in generating more precipitation over the Korean Peninsula. While the high resolution model with the same terrain data resulted in increasing of precipitation over the Korean Peninsula including the adjoining sea, the difference of the terrain data resolution only influenced the precipitation distribution of the mountainous area by increasing the amount of non-convective rain. In conclusion, the regional climate model (SNURCM) with higher resolution simulated more precipitation over the Korean Peninsula by reducing the systematic underestimation of precipitation over the Korean Peninsula.

• 대한원격탐사학회지
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• 제23권6호
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• pp.547-552
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• 2007

본 연구에서는 고해상도의 panchromatic 영상을 이용하여 저해상도의 multispectral 영상을 고해상도로 재구축하는 방법을 제시하고 있다. 제안된 방법은 저해상도와 고해상도 간의 선형 모형 사용하여 실제의 spectral 값에 부합하는 고해상도 영상을 재구축하며 두 단계로 이루어 진다. 첫 단계는 고해상도 feature와 연관된 저해상도의 선형 모형을 이용하여 최소 자승 오류 법에 의한 global 추정 과정이고 두 번째 단계는 재구축된 영상을 지역적으로 원래의 spectral 값과 일관되게 만드는 local 수정 과정이다. 본 연구에서 제안 방법을 이용하여 6m KOMPSAT-1 EOC 자료와 30m LANDSAT ETM+에 적용하였고 또한 IKONOS 1m RGB 영상 생성하였다. 실험 결과는 새로이 제시된 방법이 저해상도 Multispectral 영상의 고해상도 재구축에 탁월한 성능을 가지고 있음을 보여주었다.

• 대한원격탐사학회지
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• 제38권5_1호
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• pp.559-570
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• 2022

광학 위성 영상의 공간해상도가 낮게 되면 크기가 작은 객체들의 경우 객체 탐지의 어려움이 따른다. 따라서 본 연구에서는 위성 영상의 공간해상도를 향상시키는 초해상화(Super-resolution) 기술이 객체 탐지 정확도 향상에 대한 영향이 유의미한지 알아보고자 하였다. 쌍을 이루지 않는(unpaired) 초해상화 알고리즘을 이용하여 Sentinel-2 영상의 공간해상도를 3.2 m로 향상시켰으며, 객체 탐지 모델인 Faster-RCNN, RetinaNet, FCOS, S²ANet을 활용하여 초해상화 적용 유무에 따른 선박 탐지 정확도 변화를 확인했다. 그 결과 선박 탐지 모델의 성능 평가에서 초해상화가 적용된 영상으로 학습된 선박 탐지 모델들에서 Average Precision (AP)가 최소 12.3%, 최대 33.3% 향상됨을 확인하였고, 초해상화가 적용되지 않은 모델에 비해 미탐지 및 과탐지가 줄어듦을 보였다. 이는 초해상화 기술이 객체 탐지에서 중요한 전처리 단계가 될 수 있다는 것을 의미하고, 객체 탐지와 더불어 영상 기반의 다른 딥러닝 기술의 정확도 향상에도 크게 기여할 수 있을 것으로 기대된다.