• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.9-16
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• 2018

Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.851-863
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• 2012

Retinex algorithm enhances the contrast of image through visibility improvement. However, the conventional Retinex methods may produces color distortions due to error of hue representation and over-saturation since the methods work in RGB color space. In this paper, we propose a new Retinex algorithm with color correction, which improves contrast by using MSR(Multi-Scale Retinex) working in YCbCr color space and adaptively compensates the color saturation based on the maximum color difference table. Our algorithm maps the color difference signals to the correct gamut to prevent over-saturation phenomenon by considering the correlation between luminance and hue dependent saturation. Simulations results show that the proposed method gives better color improvement compared to the conventional methods.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.233-239
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• 2016

Color enhancement basically deals with color manipulation in digital images. Recently, the technique has become widely used as a result of the increasing use of digital cameras. Retinex-based colorenhancement algorithms are a popular technique. In this paper, retinex with bilateral filtering is proposed to improve the quality of poorly illuminated images. Generally, it consists of three main steps: first, a retinex-based algorithm with color restoration; second, transformation mapping using histogram matching; and finally, smoothing the image using a bilateral filter. The experimental results demonstrate that the proposed method can successfully enhance image contrast while avoiding the halo effect and maintaining the color distribution in the image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.126-136
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• 2007

This paper presents an enhanced algorithm for compensating the visual quality in back-light image. Current cameras do not represent all details of scene into human's eye. Saturation and underexposure are common problems in back-light image. Retinex algorithm, derived from Land's theory on human visual perception is known to be effective in enhancing the contrast. However, its weaknesses are long processing time and low contrast of bright area in back-light scene because of compensating the details of dark area. In this paper, K-Retinex algorithm is proposed to reduce the processing time and enhance the contrast in both dark and bright area. To show the superiority of proposed algorithm, we compare the processing time, local standard deviation and contrast per pixel of each area above.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.309-310
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• 2006

This paper presents an enhanced algorithm for compensating the visual quality in backlight image. Current cameras do not represent all details of scene into human's eye. Saturation and underexposure are common problems in backlight image. Retinex algorithm, derived from Land's theory on human visual perception is known to be effective in enhancing the contrast. However, its weaknesses are long processing time and low contrast of bright area in backlight scene because of compensating the details of dark area. In this paper, K-Retinex algorithm is proposed to reduce the processing time and enhance the contrast in both dark and bright area. To show the superiority of proposed algorithm, we compare the processing time and local variance of each area above.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.417-420
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• 2011

로봇이 능동적으로 행동하기 위해서는 외부 신호를 받아서 처리해야 되는데 여러 가지 센서 중에서도 영상처리가 중요해지고 있다. 하지만 영상처리를 사용하였을 경우는 예측할 수 없는 외부환경으로부터 영향을 받을 수 있다. 예를 들면 조명이 일정한 내부 환경에서는 인식이 가능하나 외부환경에서는 불가능한 경우가 있다. 그러므로 로봇산업이 발전에 중요한 축을 담당하고 있는 영상처리에 분야 중에서 논문에서는 조명이 변하는 상황을 설정해보고 그 상황을 토대로 기존의 알고리즘인 [2][3] Single-scale Retinex. [4][5] Multi-scale Retinex와 인식률을 비교해보고 Single-scale Retinex을 기반으로 단순히 Multi- scale Retinex처럼 가중치를 같이 두는 것이 아니라 상황에 따라 가중치를 다르게 주는 알고리즘 'Adaptive Two Scale Relinex Algorilhm'을 소개하겠다. 더불어 앞으로 나아가야 될 방향에 대해서도 언급하겠다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.37-38
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• 2011

본 논문은 상대적으로 대비도 차이가 크게 나타나는 역광 이미지에 대해서 Retinex 알고리즘을 적용하여 보정 했을 경우 발생하는 밝은 영역에서의 컬러 성분의 손실을 개선하기 위한 새로운 기법을 제안한다. 역광 이미지의 경우 밝은 영역과 어두운 영역에 대한 밝기 차이가 매우 크게 발생하기 때문에 Retinex 알고리즘을 이용하여 영상의 대비도를 향상시킬 경우 밝은 영역에서의 컬러 성분이 손실되는 현상이 발생한다. 이러한 손실을 보완하기 위해서 원본 영상의 밝은 영역에 해당하는 컬러 성분을 Retinex 알고리즘으로 보정된 영상에 추가해준다. Fuzzy c-means 군집화 알고리즘을 이용하여 원본 영상에서의 밝은 영역과 어두운 영역에 대하여 모든 화소의 소속 정도를 나타내는 퍼지 소속 함수를 구한다. 밝은 영역에 대해서의 컬러 성분은 원본 영상 값에 밝은 영역 퍼지 소속 함수를 적용하고, 어두운 영역에 대해서의 컬러 성분은 Retinex 복원 영상 값에 어두운 영역 퍼지 소속 함수를 이용한다. 제안하는 알고리즘의 성능 평가를 위해 역광 현상이 강하게 나타나는 자연영상들을 대상으로 적용하여 기존의 Retinex 알고리즘(MSRCR) 보다 우수한 성능을 가지고 있음을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.52-59
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• 2009

In general, methods based on histogram or a correction of gamma curve are usually utilized to enhance the contrast of captured image in the dark scene. These methods are efficient to enhance the contrast globally, however, they locally induced the low quality of image. Recently, to resolve the problem, the multi-scaled refiner algorithm improving the contrast with locally averaged lightness is proposed. However, estimating the locally averaged lightness, if there is the object with a high saturated color, the color distortion might be induced by the color of object. Thus, in this paper, the dominant chromaticity of image is estimated to correct the locally averaged lightness in multi-scaled retinex algorithm. Because the average chromaticity of image includes the chromaticity of illumination, the dominant chromaticity is estimated with dividing the average chromaticity of image by the estimated chromaticity of illumination from highlight region. In addition, to improve the lower chroma by multi-scaled retinex algorithm generally, the chroma was compensated preserving the hue in the CIELAB color space.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.376-380
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• 2015

This paper proposes new recognition method of road lanes misty day. The method enables autonomous-driving of cars and the safety of the drivers while driving with bad visibility in foggy roads. The proposed method, firstly, determines whether the foggy or not according to pixel number distributions and starting point of the fog period from input images. If it is foggy then the median filter's size of the Retinex algorithm is set to 1000 or more and it performs histogram equalization and normalization. The computer simulation results show that the proposed method can recognize better long distances and fine detection than earlier methods.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.32-39
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• 2011

The output image of the uncooled thermal infrared camera is difficult the identification of target because of the limited dynamic range and the various noises. Retinex algorithm based on the theory of the human visual perception is known to be effective contrast enhancement technique. However, the image quality is insufficient when it is adopted to the narrow dynamic range image as the infrared image. In this paper, we propose the revised retinex algorithm to enhance the contrast of the infrared image. To improve the contrast enhancement performance, we designed the new dynamic range compression function instead of log function. To reduce the noise and compensate the loss of edge, we added the contrast compensation procedure in the MSR image generation process. According to the output picture comparing and numerical analysis, the proposed algorithm shows the better contrast enhancement performance and the more suitable method for the infrared image enhancement.