• Title/Summary/Keyword: Low Resolution

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저해상도 멀티스펙트랄 자료와 고 해상도 범색 영상 융합

  • Lee, Sang-Hun
    • 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.

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Refinement of Low Resolution DEM Using Differential Interferometry

  • Kim Chang-Oh;Lee Dong-Cheon;Kim Jeong-Woo;Kim Sang-Wan;Won Joong-Sun
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.522-525
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    • 2004
  • Interferometry SAR (InSAR) is a technique to generate topographic map from complex data pairs observed by antennas at different locations. However, to obtain topographic information using InSAR is difficult task because it requires series of complicated process including phase unwrapping and precise recovery of the SAR geometry. Especially, accuracy of the DEM (Digital Elevation Model) produced by repeat pass single SAR pair could be influenced by atmospheric effect. Recently, a new InSAR technique to improve accuracy of DEM has been introduced that utilizes low resolution DEM with a number of SAR image pairs. The coarse DEM plays an important role in reducing phase unwrapping error caused by layover and satellite orbit error. In this study, we implemented DInSAR (Differential InSAR) method which combines low resolution DEMs and ERS tandem pair images. GTOPO30 DEM with 1km resolution, SRTM-3 DEM with 100m resolution, and DEM with 10m resolution derived from 1:25,000 digital vector map were used to investigate feasibility of DInSAR. The accuracy of the DEMs generated both by InSAR and DInSAR was evaluated.

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A Study on Applying the SRCNN Model and Bicubic Interpolation to Enhance Low-Resolution Weeds Images for Weeds Classification

  • Vo, Hoang Trong;Yu, Gwang-hyun;Dang, Thanh Vu;Lee, Ju-hwan;Nguyen, Huy Toan;Kim, Jin-young
    • Smart Media Journal
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    • v.9 no.4
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    • pp.17-25
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    • 2020
  • In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128 × 128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80 × 80, while bicubic interpolation is convenient for a larger image.

Gender Classification of Low-Resolution Facial Image Based on Pixel Classifier Boosting

  • Ban, Kyu-Dae;Kim, Jaehong;Yoon, Hosub
    • ETRI Journal
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    • v.38 no.2
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    • pp.347-355
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    • 2016
  • In face examinations, gender classification (GC) is one of several fundamental tasks. Recent literature on GC primarily utilizes datasets containing high-resolution images of faces captured in uncontrolled real-world settings. In contrast, there have been few efforts that focus on utilizing low-resolution images of faces in GC. We propose a GC method based on a pixel classifier boosting with modified census transform features. Experiments are conducted using large datasets, such as Labeled Faces in the Wild and The Images of Groups, and standard protocols of GC communities. Experimental results show that, despite using low-resolution facial images that have a 15-pixel inter-ocular distance, the proposed method records a higher classification rate compared to current state-of-the-art GC algorithms.

Marine Seismic Survey using a Multi-source System (다중음원을 이용한 다중채널 해양 탄성파 탐사)

  • Kim, Hyun-Do;Kim, Jin-Hoo
    • Proceedings of the Korean Society of Marine Engineers Conference
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    • 2006.06a
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    • pp.209-210
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    • 2006
  • Digital technology has been applied to marine seismic survey to develop data processing technology and multi-channel marine seismic survey. In result, high-resolution marine seismic survey ended in a success. Surveys are conducted for various purposes using various frequencies of acoustic sources. A low frequency source is used for deeper penetration and a high frequency source is used for higher resolution survey. In this study, a multi-source system was used for multi-channel marine seismic survey to acquire seismic sections of both low and high frequencies. Variations of depth of penetration and resolution would be used to achieve more accurate analysis of formations. In this study, the multi-source system consists of Bubble Pulser(400 Hz) for low frequency source and Sparker(1.5 kHz) for high frequency source.

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A high performance disparity extraction algorithm using low resolution disparity histogram (저 해상도 변위 히스토그램을 이용한 고성능 변위정보 추출 알고리듬)

  • 김남규;이광도;김형곤;차균현
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.3
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    • pp.131-143
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    • 1998
  • This paper presents a high performance disparity extraction algorithm that generate a dense and accurate disparity map using low-resolution disparity histogram. Disparity distribution of background and object areas can besegmented from low-resolution disparity histogram. These information can be used to reduce the search area and search range of the high-resolution image resulting reliable disparity information in high speed. The computationally efficient matching pixel count(MPC) similarity measure technique is useed extensively toremove the redundancies inherent in the area-based matching method, and also results robust matching at the boundary region. Resulting maches are further improved using iterative support algorithm and post processing. We have obtained good results on randomdot stereogram and real images obtained in our carmera system.

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Loss Information Estimation and Image Resolution Enhancement Technique using Low (하위 레벨 보간을 이용한 손실 정보 추정과 영상 해상도 향상 기법)

  • Kim, Won-Hee;Kim, Jong-Nam
    • 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.

Superresolution Restoration From Directional Rectangular Blurred Images (방향성 직사각형 열화 영상을 사용한 초해상도 영상복원)

  • Shin, Jeongho
    • Journal of Broadcast Engineering
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    • v.19 no.1
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    • pp.109-117
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    • 2014
  • This paper presents a superresolution restoration technique that can restore high-resolution images from differently blurred low resolution images rather than using the motion information between low-resolution images. In order to restore the super-resolution image the rotatable aperture mask lens system is proposed. The proposed technique does not need to estimate point spread function at each frame. In addition, it does not require image registration because there is no global translational motion between low resolution images. By using a rotatable rectangular aperture, two consecutive captured images provide sufficiently exclusive information for superresolution. Therefore, the proposed method can reduce the registration error between the low-resolution image as well as the calculation amount for superresolution restoration. The existing lens system of the camera can be extended to obtain a superresolution image by only adding an rotatable rectangular aperture mask. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing superresolution methods, the proposed method showed the significant improvements in the sense of spatial resolution.

Vehicle Detection Algorithm Using Super Resolution Based on Deep Residual Dense Block for Remote Sensing Images (원격 영상에서 심층 잔차 밀집 기반의 초고해상도 기법을 이용한 차량 검출 알고리즘)

  • Oh-Seol Kwon
    • Journal of Broadcast Engineering
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    • v.28 no.1
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    • pp.124-131
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    • 2023
  • Object detection techniques are increasingly used to obtain information on physical characteristics or situations of a specific area from remote images. The accuracy of object detection is decreased in remote sensing images with low resolution because the low resolution reduces the amount of detail that can be captured in an image. A single neural network is proposed to joint the super-resolution method and object detection method. The proposed method constructs a deep residual-based network to restore object features in low-resolution images. Moreover, the proposed method is used to improve the performance of object detection by jointing a single network with YOLOv5. The proposed method is experimentally tested using VEDAI data for low-resolution images. The results show that vehicle detection performance improved by 81.38% on mAP@0.5 for VISIBLE data.

Image Processing Algorithm for Crack Detection of Sewer with low resolution (저해상도 하수관거의 균열 탐지를 위한 영상처리 알고리즘)

  • Son, Byung Jik;Jeon, Joon Ryong;Heo, Gwang Hee
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.2
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    • pp.590-599
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    • 2017
  • In South Korea, sewage pipeline exploration devices have been developed using high resolution digital cameras of 2 mega-pixels or more. On the other hand, most devices are less than 300 kilo-pixels. Moreover, because 100 kilo-pixels devices are used widely, the environment for image processing is very poor. In this study, very low resolution ($240{\times}320$ = 76,800 pixels) images were adapted when it is difficult to detect cracks. Considering that the images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique was studied using digital image processing technology for low resolution images of sewage pipelines. The authors developed a program to automatically detect cracks as 6 steps based on the MATLAB functions. In this study, the second step covers an algorithm developed to find the optimal threshold value, and the fifth step deals with an algorithm to determine cracks. In step 2, Otsu's threshold for images with a white caption was higher than that for an image without caption. Therefore, the optimal threshold was found by decreasing the Otsu threshold by 0.01 from the beginning. Step 5 presents an algorithm that detects cracks by judging that the length is 10 mm (40 pixels) or more and the width is 1 mm (4 pixels) or more. As a result, the crack detection performance was good despite the very low-resolution images.