• 대한임베디드공학회논문지
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• 제13권1호
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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.

• 센서학회지
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• 제19권2호
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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.

• ETRI Journal
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• 제32권4호
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• pp.596-602
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• 2010

This paper proposes a computationally efficient learning-based super-resolution algorithm using k-means clustering. Conventional learning-based super-resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.

• 한국정보통신학회논문지
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• 제17권7호
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• pp.1672-1686
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• 2013

본 논문에서는 이산 웨이블릿 변환(discrete wavelet transform: DWT)을 이용한 단일영상 기반의 초고해상도 기법(super-resolution)과, 복수영상 기반의 초고해상도 기법을 제시하고 두 기법을 혼합한 새로운 초고해상도 기법 기법을 제안한다. 기존의 단일 영상 기반의 초고해상도 기법의 경우 처리 시간이 빠르다는 장점이 있으나 영상 보간 시 사용할 수 있는 정보량이 제한적이다. 또한 기존 복수영상 기반의 초고해상도 기법은 단일 영상을 사용했을 경우보다 영상의 보간 시 많은 정보를 사용할 수 있으나 영상의 내용에 따라 기법의 적용이 제한적이고, 컷(cut)의 경계 부근에서 기법의 성능이 매우 떨어지는 단점이 있다. 제안된 기법에서는 컷 검출(cut-detection) 기법을 통해 각 장면의 경계부근에서 적응적으로 단일영상 기반의 초고해상도 기법을 사용한다. 또한 움직임 벡터의 정규화 및 블록 단위의 윤곽선(edge) 패턴 분석을 통해 여러 제한조건에 강한 복수 영상 기반의 초고해상도 기법을 제안한다. 실험을 통하여 제안된 기법이 객관적, 주관적으로 기존의 기법보다 우수한 성능을 보이는 것을 확인하였다.

• 한국군사과학기술학회지
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• 제14권4호
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• pp.663-668
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• 2011

Although multi-frame super resolution algorithm has many merits but it demands too much calculation time. Researches have shown that image processing time can be reduced using a CUDA(Compute unified device architecture) which is one of GPGPU(General purpose computing on graphics processing unit) models. In this paper, we show that the processing time of multi-frame super resolution algorithm can be reduced by employing the CUDA. It was applied not to the whole parts but to the largest time consuming parts of the program. The simulation result shows that using a CUDA can reduce an operation time dramatically. Therefore it can be possible that multi-frame super resolution algorithm is implemented in real time by using libraries of image processing algorithms which are made by a CUDA.

• 한국통신학회논문지
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• 제38A권4호
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• pp.310-317
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• 2013

본 논문에서는 실시간 위치 추적 시스템 (RTLS: real-time locating system)을 위한 ESPRIT 기반의 초 분해능 지연 시간 추정 (super-resolution time delay estimation) 알고리즘을 개발하고 여러 가지 다중 경로 환경에서 성능을 분석한다. 지연 시간 추정을 위한 기존의 코릴레이션 방식은 다중 경로들의 지연 시간의 차이가 한 칩 이내일 경우 성능이 급격히 저하되는 문제점이 있다. 이러한 문제를 해결하기 위해 대표적인 초 분해능 도래각 추정 알고리즘인 ESPRIT을 지연 시간 추정에 적용하여 주파수 영역 초 분해능 지연 시간 추정 알고리즘을 개발하고 여러 가지 다중 경로 환경에서 알고리즘의 성능을 분석한다.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2204-2212
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• 2022

Recently, the demand for alpha imaging detectors for quantifying the distributions of alpha particles has increased in various fields. This study aims to reconstruct a high-resolution image from an alpha imaging detector by applying a super-spatial resolution method combined with the maximum-likelihood expectation maximization (MLEM) algorithm. To perform the super-spatial resolution method, several images are acquired while slightly moving the detector to predefined positions. Then, a forward model for imaging is established by the system matrix containing the mechanical shifts, subsampling, and measured point-spread function of the imaging system. Using the measured images and system matrix, the MLEM algorithm is implemented, which converges towards a high-resolution image. We evaluated the performance of the proposed method through the Monte Carlo simulations and phantom experiments. The results showed that the super-spatial resolution method was successfully applied to the alpha imaging detector. The spatial resolution of the resultant image was improved by approximately 12% using four images. Overall, the study's outcomes demonstrate the feasibility of the super-spatial resolution method for the alpha imaging detector. Possible applications of the proposed method include high-resolution imaging for alpha particles of in vitro sliced tissue and pre-clinical biologic assessments for targeted alpha therapy.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.363-371
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• 2014

In this paper, a new super-resolution algorithm is proposed by using successive frames for generating high-resolution frames with better quality than those generated by other conventional interpolation methods. Generally, each frame used for super-resolution must only have global translation and motions of sub-pixel unit to generate good result. However, the newly proposed MSR algorithm in this paper is exempt from such constraints. The proposed algorithm consists of three main processes; motion estimation for image registration, normalization of motion vectors, and pattern analysis of edges. The experimental results show that the proposed algorithm has better performance than other conventional algorithms.

• 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.

• 한국통신학회논문지
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• 제37권6A호
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• pp.464-472
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• 2012

본 논문에서는 연속된 프레임을 갖는 영상의 프레임간 움직임 추정 기법을 응용하여 고해상도 영상을 생성하는 초해상도 기법을 제안한다. 단일 영상을 이용한 초해상도 기법의 경우 영상에서의 고주파 대역을 찾기 위해 확률 및 이산 웨이블릿 변환(discrete wavelet transform: DWT) 기반 등 다양한 방법이 제시되었으나, 연산에 사용할 수 있는 정보가 제한적이라는 문제가 존재한다. 이러한 문제를 해결하기 위해 연속된 프레임을 이용한 초해상도 기법이 다양하게 제안되었다. 연속 프레임 기반 초해상도 기법의 핵심인 입력 저해상도 영상 간 정합(registration)의 정확도는 초해상도 기법의 결과에 큰 영향을 갖는다. 본 논문에서는 영상 간 정합의 정확도를 높이기 위하여 6-tap FIR(finite impulse response) 필터를 부화소(sub-pixel) 단위의 정합에 사용한다. 실험을 통하여 제안하는 기법의 결과영상이 기존의 최단입점(nearest neighborhood), 이중선형(bi-linear), 고등차수(bi-cubic) 보간법 보다는 우수하고 DWT 기반의 초해상도 기법과는 비슷한 성능을 가진다는 것을 확인할 수 있었다.