• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.733-734
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• 2006

The Face Area Detection has an extensive error range of abstraction probabilities by image illuminations and background conditions. In this paper, to reduce error ranges of abstraction probabilities by factors such as illuminations and backgrounds, we made use of Retinex and the Face Area Detection algorithm together. In comparison with other previous methods[4], our proposed algorithm showed stabler and elevated detection rate.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.269-272
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• 2005

The principal objective of enhancement methods is to process an image so that the result is more suitable than the original image for a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compare the structure of ICECA(Image Contrast Enhancement technique using Clustering Algorithm) with the structures of HE(Histogram Equalization), BBHE(Brightness preserving Bi-Histogram Equalization), and Multi -Scale Retinex(MSR). We compared performances of image enhancement methods by applying these methods to a set of diverse images.

• Journal of Multimedia Information System
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• 제5권2호
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• pp.87-90
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• 2018

Various approaches have been proposed to convert low dynamic range (LDR) to high dynamic range (HDR). Of these approaches, the Multi Scale Fusion (MSF) algorithm based on Laplacian pyramid decomposition is used in many applications and demonstrates its usefulness. However, the pyramid fusion technique has no means for controlling the luminance component because the total number of pixels decreases as the pyramid rises to the upper layer. In this paper, we extract the reflection light of the image based on the Retinex theory and generate the weight map by adjusting the reflection component. This weighting map is applied to achieve an MSF-like effect during image fusion and provides an opportunity to control the brightness components. Experimental results show that the proposed method maintains the total number of pixels and exhibits similar effects to the conventional method.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1761-1768
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• 2009

In this paper, we implemented online real time strategy game using hand as the mouse in augmented reality. Also, we introduced the algorithm for detecting hand direction, finding fingertip of the index finger and counting the number of fingers for interaction between users and the virtual objects. The proposed method increases the reality of the game by combining the real world and the virtual objects. Retinex algorithm is used to remove the effect of illumination change. The implementation of the virtual reality in the online environment enables to extend the applicability of the proposed method to the areas such as online education, remote medical treatment, and mobile interactive games.

• 전기전자학회논문지
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• 제22권4호
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• pp.1188-1194
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• 2018

This paper proposes an image enhancement-based license plate detection algorithm to improve the overall performance of system. Non-uniform illumination conditions have huge impact on overall plate detection system accuracy. In this paper, we propose an algorithm for color image enhancement-based license plate detection for improving accuracy of images degraded by excessively strong and low sunlight. Firstly, the image is enhanced by Multi-Scale Retinex algorithm. Secondly, a plate detection method is employed to take advantage of geometric properties of connected components, which can significantly reduce the undesired plate regions. Finally, intersection over union method is applied for detecting the accurate location of number plate. Experimental results show that the proposed method significantly improves the accuracy of plate detection system.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.370-376
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• 2023

Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.443-445
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• 2022

광원 자체의 밝기가 낮거나 드리워진 그림자 등의 이유로 어두운 영역을 포함하고 있는 저조도 영상으로 인해 물체의 식별이 어려운 상황을 일상생활에서 겪게 된다. 본 논문에서는 조명 성분의 영향을 줄이고 객체의 특징을 표현하는 반사 성분을 강조하여 화질을 개선한다. 또한 촬영하는 카메라와 영상의 물체 사이의 상대적인 움직임으로 발생하는 흐릿한 영역을 최대한 제거해주고 잡음까지 보정이 되는 Stack-attention 기법을 제안한다.

• 정보처리학회논문지A
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• 제16A권6호
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• pp.473-480
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• 2009

본 논문에서는 역광 및 어두운 영상에 효과적인 칼라 영상 개선 알고리즘을 제안하고 PXA255 ARM 프로세서 기반 임베디드 리눅스 환경 에 구현하는 것을 목적으로 한다. 기존의 영상 개선 알고리즘 중에서 레티넥스는 역광 및 어두운 영상에 효과적이나 연산량이 많아 임베디드 시스템에서의 구현이 적합하지 못하다. 따라서 레티넥스와 동등한 영상 개선 효과를 갖으면서 연산량이 적어 임베디드 시스템에서 구현 가능한 영상 개선 알고리즘을 제안한다. 제안된 영상 향상 알고리즘은 HSV 색 모델로 변환한 다음 명도 성분과 채도 성분 영상에 각각 영상 생성 모 델과 감마 보정을 적용하여 구현하였다. 또한, 제안한 알고리즘을 PXA255 ARM 프로세서에 최적화 과정을 통하여 연산량을 감소하였다. 정량 적인 방법과 정성적인 방법을 통하여 제안된 알고리즘의 성능을 평가 하였다. 평가 결과 연산량은 감소하였으나 밝기와 명도 대비를 향상시키는 것을 확인하였다.

• 한국통신학회논문지
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• 제39C권1호
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• pp.28-35
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• 2014

국경 GOP(General Out Post)나 해안선 지역에 설치되어 운용되는 국방용 감시카메라는 급변하는 기상 및 조명환경에 의해 영상의 화질이 왜곡되고 열화되는 현상이 빈번하게 발생한다. 본 논문에서는 이러한 기상 및 조명환경 변화에 적응적인 화질개선 알고리즘을 제안한다. 기존 화질개선 알고리즘은 급변하는 환경조건에서 영상에 따라 극심한 성능편차를 보이는 문제점이 있는데, 이를 해결하기 위해 레티넥스(Retinex) 모델을 근간으로 환경변화에 적응적인 보정곡선을 이용하여 안개 및 저조도 환경에서 영상의 가시성을 향상시켜 높은 대비와 색상을 자연스럽게 재현하였고 실시간 환경변화에 적응토록 하였다. 또한, HSV 색모델을 가중 혼합하여 색 항상성 (Color Constancy)을 유지시켰으며, 개선과정 중 잡음(Noise)을 제거하여 보다 선명한 영상을 출력토록 하였다. 제안 알고리즘은 실험을 통해 기존 알고리즘 대비 주관적 평가인 MOS 1단계 향상효과 및 객관적 평가인 PSNR 15% 성능향상의 우수성을 입증하였다. 향후 국방감시 카메라 및 시스템에 적용되어 GOP나 해안선 지역의 열악한 기상조건으로부터 열화된 영상을 개선하여 적 침투 및 경계감시 식별에 도움을 주어 시스템의 신뢰성 향상에 기여할 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.