• Title, Summary, Keyword: super-resolution

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Consecutive-Frame Super-Resolution considering Moving Object Region

  • Cho, Sung Min;Jeong, Woo Jin;Jang, Kyung Hyun;Choi, Byung In;Moon, Young Shik
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.3
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    • pp.45-51
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    • 2017
  • In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

Super-resolution image enhancement by Papoulis-Gerchbergmethod improvement (Papoulis-Gerchberg 방법의 개선에 의한 초해상도 영상 화질 향상)

  • Jang, Hyo-Sik;Kim, Duk-Gyoo;Jung, Yoon-Soo;Lee, Tae-Gyoun;Won, Chul-Ho
    • Journal of Sensor Science and Technology
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    • v.19 no.2
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    • pp.118-123
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    • 2010
  • This paper proposes super-resolution reconstruction algorithm for image enhancement. Super-resolution reconstruction algorithms reconstruct a high-resolution image from multi-frame low-resolution images of a scene. Conventional super- resolution reconstruction algorithms are iterative back-projection(IBP), robust super-resolution(RS)method and standard Papoulis-Gerchberg(PG)method. However, traditional methods have some problems such as rotation and ringing. So, this paper proposes modified algorithm to improve the problem. Experimental results show that this proposed algorithm solve the problem. As a result, the proposed method showed an increase in the PSNR for traditional super-resolution reconstruction algorithms.

Construction of Super-Resolution Convolutional Neural Network Model for Super-Resolution of Temperature Data (기온 데이터 초해상화를 위한 Super-Resolution Convolutional Neural Network 모델 구축)

  • Kim, Yong-Hoon;Im, Hyo-Hyuk;Ha, Ji-Hun;Park, Kun-Woo;Kim, Yong-Hyuk
    • Journal of the Korea Convergence Society
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    • v.11 no.8
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    • pp.7-13
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    • 2020
  • Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.

Low Complexity Super Resolution Algorithm for FOD FMCW Radar Systems (이물질 탐지용 FMCW 레이더를 위한 저복잡도 초고해상도 알고리즘)

  • Kim, Bong-seok;Kim, Sangdong;Lee, Jonghun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.13 no.1
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    • pp.1-8
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    • 2018
  • This paper proposes a low complexity super resolution algorithm for frequency modulated continuous wave (FMCW) radar systems for foreign object debris (FOD) detection. FOD radar has a requirement to detect foreign object in small units in a large area. However, The fast Fourier transform (FFT) method, which is most widely used in FMCW radar, has a disadvantage in that it can not distinguish between adjacent targets. Super resolution algorithms have a significantly higher resolution compared with the detection algorithm based on FFT. However, in the case of the large number of samples, the computational complexity of the super resolution algorithms is drastically high and thus super resolution algorithms are difficult to apply to real time systems. In order to overcome this disadvantage of super resolution algorithm, first, the proposed algorithm coarsely obtains the frequency of the beat signal by employing FFT. Instead of using all the samples of the beat signal, the number of samples is adjusted according to the frequency of the beat signal. By doing so, the proposed algorithm significantly reduces the computational complexity of multiple signal classifier (MUSIC) algorithm. Simulation results show that the proposed method achieves accurate location even though it has considerably lower complexity than the conventional super resolution algorithms.

Super-Resolution Image Processing Algorithm Using Hybrid Up-sampling (하이브리드 업샘플링을 이용한 베이시안 초해상도 영상처리)

  • Park, Jong-Hyun;Kang, Moon-Gi
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.2
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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.

Super Resolution Image Reconstruction using the Maximum A-Posteriori Method

  • Kwon Hyuk-Jong;Kim Byung-Guk
    • Proceedings of the KSRS Conference
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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.

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SUPER RESOLUTION RECONSTRUCTION FROM IMAGE SEQUENCE

  • Park Jae-Min;Kim Byung-Guk
    • Proceedings of the KSRS Conference
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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.

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Resolution enhanced integral imaging using super-resolution image reconstruction algorithm (초해상도 영상복원을 이용한 집적영상의 해상도 향상)

  • Hong, Kee-Hoon;Park, Jae-Hyeung;Lee, Byoung-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.10B
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    • pp.1124-1132
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    • 2009
  • We proposed a new method to improve the resolution of elemental image set in the integral imaging system using super-resolution image reconstruction method. Adjacent elemental images have same image region which is projected from the common area of object. These projected images in the elemental image can be used for low resolution images of super-resolution method. Two methods for resolution improvement of elemental image set using super-resolution method are proposed. One is super-resolution among the elemental image sets and the other is among the elemental images. Simulation results are compared with resolution improved elemental image set using interpolated method.

Land Cover Super-resolution Mapping using Hopfield Neural Network for Simulated SPOT Image

  • Nguyen, Quang Minh
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.6_2
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    • pp.653-663
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    • 2012
  • Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

Untact Face Recognition System Based on Super-resolution in Low-Resolution Images (초고해상도 기반 비대면 저해상도 영상의 얼굴 인식 시스템)

  • Bae, Hyeon Bin;Kwon, Oh Seol
    • Journal of Korea Multimedia Society
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    • v.23 no.3
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    • pp.412-420
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    • 2020
  • This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.