• 전기학회논문지
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• 제57권2호
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• pp.294-302
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• 2008

In this paper, we present a new image up-sampling method which registers low resolution images to the high resolution grid when Bayesian super-resolution image processing is performed. The proposed up-sampling method interpolates high-resolution pixels using high-frequency data lying in all the low resolution images, instead of up-sampling each low resolution image separately. The interpolation is based on B-spline non-uniform re-sampling, adjusted for the super-resolution image processing. The experimental results demonstrate the effects when different up-sampling methods generally used such as zero-padding or bilinear interpolation are applied to the super-resolution image reconstruction. Then, we show that the proposed hybird up-sampling method generates high-resolution images more accurately than conventional methods with quantitative and qualitative assess measures.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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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.

• 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.

• 한국컴퓨터그래픽스학회논문지
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• 제25권3호
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• pp.21-29
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• 2019

본 논문에서는 구조-텍스처 분할 기법을 기반으로 위성영상을 분할 융합하여 공간 해상도를 개선시키는 프레임워크를 제시한다. 위성영상은 센서가 감지하는 파장에 따라 다양한 공간해상도를 가진다. 전정 영상 (panchromatic image)은 일반적으로 높은 공간해상도를 가지지만 단일 흑백컬러를 가지고 있는 반면, 다중분광 영상 (multi-spectral image)나 적외선 영상은 전정 영상에 비해 낮은 공간해상도를 가지지만 다양한 분광 밴드정보와 열 정보를 가지고 있다. 본 논문에서는 다중분광 영상이나 적외선 영상의 공간 해상도를 향상시키기 위해 영상의 디테일이 텍스처 영상에만 존재한다는 것에 착안하여 본 프레임워크를 고안하였다. 고안된 프레임워크에서는 저해상도 영상과 고해상도 영상이 구조 영상과 텍스처 영상으로 분할된 뒤, 저해상도 구조영상은 고해상도 구조 영상을 참조하여 가이디드 필터링 된다. 구조-텍스처 영상 모델에 따라 필터링된 저해상도 영상의 구조 영역과 고해상도 영상의 텍스처 영역을 픽셀 단위로 더해져서 최종 영상이 생성된다. 생성된 영상은 저해상도 영상의 밴드와 고해상도 영상의 디테일을 포함한다. 제시하는 방법은 분광해상도와 공간해상도를 모두 보존할 수 있음을 실험적으로 확인하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (B)
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• pp.715-717
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• 2004

This paper proposes a new reconstruction method of high-resolution facial image from a low-resolution facial image based on a recursive error back-projection of top-down machine learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes, In addition to, a recursive error back-projection is applied to improve the accuracy of synthesized high-resolution facial image. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to reconstruct high-resolution facial images from low-resolution one captured at a distance.

• 천문학회보
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• 제44권2호
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• pp.70.4-70.4
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• 2019

Image super-resolution (SR) is a technique that enhances the resolution of a low resolution image. In this study, we use three SR models (RCAN, ProSRGAN and Bicubic) for enhancing solar SDO/HMI magnetograms using deep learning. Each model generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). The pixel resolution of HMI is about 0.504 arcsec. Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained three models with HMI images in 2014 and test them with HMI images in 2015. We find that the RCAN model achieves higher quality results than the other two methods in view of both visual aspects and metrics: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is also much better than the conventional bi-cubic interpolation. We apply this model to a full-resolution SDO/HMI image and compare the generated image with the corresponding Hinode NFI magnetogram. As a result, we get a very high correlation (0.92) between the generated SR magnetogram and the Hinode one.

• 대한전자공학회논문지SP
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• 제43권2호
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• pp.12-18
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• 2006

이 논문은 저해상도 동영상에서 자동화된 방식으로 한 장의 좋은 화질의 고해상도 영상을 얻는 방안을 제시한다. 여러 장의 저해상도 영상을 이용하여 고해상도 영상을 얻는 방법이 한 장의 저해상도 영상만을 이용하는 전통적인 보간 방법에 비해 좋은 결과를 보이기 위해서는 입력 영상들이 공통된 고해상도 격자에 잘 정합되어야 하므로, 정합오차를 충분히 고려하여 입력영상들을 주의 깊게 선택한다. 본 논문에서는 움직임 보상된 저해상도 영상들로부터 얻어진 통계적 특성을 활용하여 입력 영상 후보들의 입력 영상으로서의 적합성을 평가한다. 고해상도 영상획득모델로부터 움직임 보상오차의 최대값을 추정한다. 입력 영상 후보의 움직임 보상오차가 추정된 움직임 보상오차의 최대값보다 크면 입력 영상후보는 선정에서 제외된다. 선정된 적절한 유효 입력 영상 후보의 수와 움직임 보상오차의 통계치를 고려하여 최종 입력 영상들을 선별한다. 입력 영상 선별부에서 최종적으로 선별된 입력 영상들은 뒤따르는 고해상도 영상복원부로 입력된다. 제안된 방식은 사용자의 간섭없이 저해상도 동영상에서 효과적으로 입력 영상들을 선별하여 좋은 화질의 고해상도 영상을 얻는 응용에 사용될 것으로 기대된다.

• 천문학회보
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• 제44권1호
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• pp.52.1-52.1
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• 2019

The Helioseismic and Magnetic Imager (HMI) is the instrument of Solar Dynamics Observatory (SDO) to study the magnetic field and oscillation at the solar surface. The HMI image is not enough to analyze very small magnetic features on solar surface since it has a spatial resolution of one arcsec. Super resolution is a technique that enhances the resolution of a low resolution image. In this study, we use a method for enhancing the solar image resolution using a Deep-learning model which generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained a model based on a very deep residual channel attention networks (RCAN) with HMI images in 2014 and test it with HMI images in 2015. We find that the model achieves high quality results in view of both visual and measures: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is much better than the conventional bi-cubic interpolation. We will apply this model to full-resolution SDO/HMI and GST magnetograms.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(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 a set of low resolution images and compared the results with those from other known interpolation methods.