• Title/Summary/Keyword: high resolution image

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Application of Deep Learning to Solar Data: 6. Super Resolution of SDO/HMI magnetograms

  • Rahman, Sumiaya;Moon, Yong-Jae;Park, Eunsu;Jeong, Hyewon;Shin, Gyungin;Lim, Daye
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.1
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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.

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A Study on Feature Extraction Using High-Resolution Satellite Image Data (고해상도 위성 영상데이터를 이용한 지형요소 추출에 관한 연구)

  • 김상철;신석효;안기원;이건기;서두천
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2003.04a
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    • pp.181-185
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    • 2003
  • Recently, in accordance with supplying high-resolution satellite images which as IKONOS, KVR-1000, and Quick Bird, the use of satellite images have increased in the study which extraction of features from high-resolution satellite images is becoming a new research focus. In this study, using generally involves such as image segmentation, filtering and sobel operator and thinning in image processing for extraction of feature from satellite image. We apply this method to extraction of feature which need to the revision of map from high-resolution IKONOS satellite image data, we verified the capability of extraction of feature and application using satellite image and proposed a plan for the study in the future.

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A study on DR image restoration using dual sensor (이중센서를 이용한 DR 영상 개선에 관한 연구)

  • 백승권;이태수;민병구
    • 제어로봇시스템학회:학술대회논문집
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    • 1988.10a
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    • pp.725-728
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    • 1988
  • Image restoration technique using dual sensor is presented in this paper. Digital Radiography image (1024xlO24) is obtained by conventional resolution sensor. We also obtain local DR image data by high resolution sensor. Two dimensional maximum entropy power spectrum estimation (2-D ME PSE) is applied to low resolution image and high resolution image for the purpose of the power spectrum estimation of each image. A class of linear algebraic restoration filter, parametric projection filter (PPF), is derived from the power spectrums of each image. It is shown that the noise energy may be considerably reduced through the PPF.

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Image Fusion Framework for Enhancing Spatial Resolution of Satellite Image using Structure-Texture Decomposition (구조-텍스처 분할을 이용한 위성영상 융합 프레임워크)

  • Yoo, Daehoon
    • Journal of the Korea Computer Graphics Society
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    • v.25 no.3
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    • pp.21-29
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    • 2019
  • This paper proposes a novel framework for image fusion of satellite imagery to enhance spatial resolution of the image via structure-texture decomposition. The resolution of the satellite imagery depends on the sensors, for example, panchromatic images have high spatial resolution but only a single gray band whereas multi-spectral images have low spatial resolution but multiple bands. To enhance the spatial resolution of low-resolution images, such as multi-spectral or infrared images, the proposed framework combines the structures from the low-resolution image and the textures from the high-resolution image. To improve the spatial quality of structural edges, the structure image from the low-resolution image is guided filtered with the structure image from the high-resolution image as the guidance image. The combination step is performed by pixel-wise addition of the filtered structure image and the texture image. Quantitative and qualitative evaluation demonstrate the proposed method preserves spectral and spatial fidelity of input images.

High Resolution Reconstruction of Multispectral Imagery with Low Resolution (저해상도 Multispectral 영상의 고해상도 재구축)

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.23 no.6
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    • pp.547-552
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    • 2007
  • 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. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.

Reconstruction of High-Resolution Facial Image Based on Recursive Error Back-Projection of Top-Down Machine Learning (하향식 기계학습의 반복적 오차 역투영에 기반한 고해상도 얼굴 영상의 복원)

  • Park, Jeong-Seon;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.34 no.3
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    • pp.266-274
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    • 2007
  • This paper proposes a new reconstruction method of high-resolution facial image from a low-resolution facial image based on top-down machine learning and recursive error back-projection. A face is represented by a linear combination of prototypes of shape and that of texture. With the shape and texture information of each pixel in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those that of texture by solving least square minimizations. Then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition, a recursive error back-projection procedure is applied to improve the reconstruction accuracy of 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 images captured at a distance.

MRI Image Super Resolution through Filter Learning Based on Surrounding Gradient Information in 3D Space (3D 공간상에서의 주변 기울기 정보를 기반에 둔 필터 학습을 통한 MRI 영상 초해상화)

  • Park, Seongsu;Kim, Yunsoo;Gahm, Jin Kyu
    • Journal of Korea Multimedia Society
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    • v.24 no.2
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    • pp.178-185
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    • 2021
  • Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics (국부 통계 특성을 이용한 적응 MAP 방식의 고해상도 영상 복원 방식)

  • Kim, Kyung-Ho;Song, Won-Seon;Hong, Min-Cheol
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.31 no.12C
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    • pp.1194-1200
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    • 2006
  • In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve the edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution, and the smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.

Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Subbands and the Spatial Domain Input Image

  • Anbarjafari, Gholamreza;Demirel, Hasan
    • ETRI Journal
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    • v.32 no.3
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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.

Resolution Merge of SPOT-5 Image for National Land Monitoring (국토모니터링을 위한 SPOT-5 위성영상 융합)

  • Park, Kyeong-Sik;Choi, Seok-Keun;Lee, Jae-Kee
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2007.04a
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    • pp.141-144
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    • 2007
  • Satellite image for national land monitoring is required high resolution and natural color with multi spectral band. the image is expensive as higher resolution. We need cheap image relatively in economic viewpoint but the image serves sufficient resolution to monitor national land. We merged two images to one image and evaluated the result. the two images which are used at the merge test are high resolution(2.5m per pixel) panchromatic and low resolution(10m per pixel) multi spectral image of SPOT-5 satellite. The result of this study. We made the merge image to have sufficient resolution for national monitoring.

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