• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.535-546
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• 2010

Digital images have undesired color casts due to various illumination conditions and intrinsic characteristics of cameras. Since the color casts in the images deteriorate performance of color representations, color correction is required for further analysis of images. In this paper, an algorithm for detection and removal of color casts is presented. The proposed algorithm consists of four steps: retrieving similar image using color by correlation, extraction of near neutral color regions, kernel density estimation, and removal of color casts. Ambiguities in near neutral color regions are excluded based on kernel density estimation by the color by correlation algorithm. The method determines whether there are color casts by chromaticity distributions in near neutral color regions, and removes color casts for color constancy. Experimental results suggest that the proposed method outperforms the gray world algorithm and the color by correlation algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.276-283
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• 2002

This paper proposes a new color constancy algorithm using the maximum luminance surface. This method uses a linear model which represents the characteristics of human visual system. The most important process of linear model is the estimation of the spectral distributions of illumination from an input image. To estimate of the spectral distributions of illumination from an input image, we first estimate spectral distribution functions of reflected light on the brightest surface. Then, we estimate surface reflectance functions corresponding to the maximum luminance surface using a principal component analysis of the given munsell chips. We finally estimate the spectral distributions of illumination in an image. Using an estimated illumination, we recover an image by scaling it regularly for the lightness calibration. From the experimental results, the proposed method was effective in recovering the color images compared with others.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.84-94
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• 2006

The color information in an image changes as the illuminant condition varies. The mechanism to find canonical color of an object by estimating illumination color in an image is generally referred as color constancy. In color constancy, computing robust and precise dichromatic line is most important to estimate illumination chromaticity. In this paper, a novel approach to estimate the color of a single illuminant for noisy and micro-textured images is introduced. An accurate dichromatic line is found by using Dichromatic Line Space (DLS), proposed in this paper. which has information about diffuse chromaticity and illumination chromaticity.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.457-464
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• 2021

The advent of the big data era has enabled the rapid development of deep learning that learns rules by itself from data. In particular, the performance of CNN algorithms has reached the level of self-adjusting the source data itself. However, the existing image processing method only deals with the image data itself, and does not sufficiently consider the heterogeneous environment in which the image is generated. Images generated in a heterogeneous environment may have the same information, but their features may be expressed differently depending on the photographing environment. This means that not only the different environmental information of each image but also the same information are represented by different features, which may degrade the performance of the image analysis model. Therefore, in this paper, we propose a method to improve the performance of the image color constancy model based on Adversarial Learning that uses image data generated in a heterogeneous environment simultaneously. Specifically, the proposed methodology operates with the interaction of the 'Domain Discriminator' that predicts the environment in which the image was taken and the 'Illumination Estimator' that predicts the lighting value. As a result of conducting an experiment on 7,022 images taken in heterogeneous environments to evaluate the performance of the proposed methodology, the proposed methodology showed superior performance in terms of Angular Error compared to the existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.83-90
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• 2023

Blurred image is an important factor in lowering image recognition rates in Computer vision. This mainly occurs when the camera is unstablely out of focus or the object in the scene moves quickly during the exposure time. Blurred images greatly degrade visual quality, weakening visibility, and this phenomenon occurs frequently despite the continuous development digital camera technology. In this paper, it replace the modified building module based on the Deep multi-patch neural network designed with convolution neural networks to capture details of input images and Attention techniques to focus on objects in blurred images in many ways and strengthen the image. It measures and assigns each weight at different scales to differentiate the blurring of change and restores from rough to fine levels of the image to adjust both global and local region sequentially. Through this method, it show excellent results that recover degraded image quality, extract efficient object detection and features, and complement color constancy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.55-62
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• 2011

In this paper, a temporal color and luminance variation suppression algorithm for a digital video sequence is proposed by considering time-varying light source. When a video sequence is sampled with the periodically emitting illuminant and with a short exposure time, the color rolling phenomenon occurs, where the color and the luminance of the image periodically change from field to field. In conventional signal processing techniques, the luminance variation remaining in the resultant video sequence degrades the constancy of the image sequence. In the proposed method, we obtain video sequences with constant luminance and color by compensating for the inter-field luminance variation. Based on a motion detection technique, the amount of the luminance variation for each channel is estimated on the background of the sequence without the effects of moving objects. The experimental results clearly show that our strategy efficiently estimated the illuminant change without being affected by moving objects, and the variations were efficiently reduced.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.110-121
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• 2016

The primary aim of the color constancy algorithm is to estimate illuminant chromaticity. There are various statistical-based, learning-based and combinational-based color constancy algorithms already exist. However, the statistical-based algorithms can only perform well on images that satisfy certain assumptions, learning-based methods are complex methods that require proper preprocessing and training data, and combinational-based methods depend on either pre-determined or dynamically varying weights, which are difficult to determine and prone to error. Therefore, this paper presents a new optimization based illuminant estimation method which is free from complex preprocessing and can estimate the illuminant under different environmental conditions. A strong color cast always has an odd standard deviation value in one of the RGB channels. Based on this observation, a cost function called the degree of illuminant tinge(DIT) is proposed to determine the quality of illuminant color-calibrated images. This DIT is formulated in such a way that the image scene under standard illuminant (d65) has lower DIT value compared to the same scene under different illuminant. Here, a swarm intelligence based particle swarm optimizer(PSO) is used to find the optimum illuminant of the given image that minimizes the degree of illuminant tinge. The proposed method is evaluated using real-world datasets and the experimental results validate the effectiveness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.251-258
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• 2004

This paper proposes a method for estimating the illuminant chromaticity using the distributions of the camera responses obtained by a CCD camera in a real-world scene. Illuminant estimation using a highlight method is based on the geometric relation between a body and its surface reflection. In general, the pixels in a highlight region are affected by an illuminant geometric difference, camera quantization errors, and the non-uniformity of the CCD sensor. As such, this leads to inaccurate results if an illuminant is estimated using the pixels of a CCD camera without any preprocessing. Accordingly, to solve this problem the proposed method analyzes the distribution of the CCD camera responses and selects pixels using the Mahalanobis distance in highlight regions. The use of the Mahalanobis distance based on the camera responses enables the adaptive selection of valid pixels among the pixels distributed in the highlight regions. Lines are then determined based on the selected pixels with r-g chromaticity coordinates using a principal component analysis(PCA). Thereafter, the illuminant chromaticity is estimated based on the intersection points of the lines. Experimental results using the proposed method demonstrated a reduced estimation error compared with the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.28-35
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• 2014

Surveillance cameras in national border and coastline area often occur the video distortion because of rapidly changing weather and light environments. It is positively necessary to enhance the distorted video quality for keeping surveillance. In this paper, we propose an adaptive video enhancement algorithm in the various environment changes. To solve an unstable performance problem of the existing method, the proposed method is based on Retinex algorithm and uses enhanced curves which is adapted in foggy and low-light conditions. In addition, we mixture the weighted HSV color model to keep color constancy and reduce noise to obtain clear images. As a results, the proposed algorithm improves the performance of well-balanced contrast enhancement and effective color restoration without any quality loss compared with the existing algorithm. We expect that this method will be used in surveillance camera systems and offer help of national defence with reliability.

• Proceedings of the Korean Journal of Food and Nutrition Conference
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• 2001.12a
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• pp.130-130
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• 2001

버섯은 특유의 향과 풍미 뿐 아니라 단백질, 다당류, 비타민, 무기질 등을 고루 함유란 저칼로리 영양식품으로서 가치가 높고, 최근에는 생체방어, 항상성 유지, 질병의 회복뿐만 아니라 암, 뇌졸중, 심장병 등의 성인병에 대한 예방과 개선효과가 있는 것으로 알려져 기능성 식품소재로서 활용가치가 높아지고 있다. 그러나 버섯류는 대부분 생체 또는 건조품으로 소비되고 있으며 재배기술이 개선되어 점진적으로 생산량이 증가되나. 수요가 이를 따르지 못해 계절적 공급과잉으로 가격파동이 우려된다. 따라서 출하조절과 버섯 수요를 확대를 위하여 영지, 표고, 눈꽃동충하초 그리고 번데기동충하초를 이용한 버섯젤리의 제조와 제품의 색도, 물성 및 기호도를 조사 비교하였다. 버섯 젤리 제조를 위한 추출액의 적정혼합비율을 선발한 결과, 영지버섯 젤리는 버섯추출액 85%, 대추추출액 10%, 황기추출액 5%를 혼합했을 때, 표고버섯 젤리는 버섯추출액 80%, 대추추출액 10%, 감초추출액 5%, 오미자추출액 5%를 혼합했을 때, 눈꽃동충하초와 번데기동충하초 젤리는 버섯추출액 85%, 대추추출액 10%, 감초추출액 5%를 혼합했을 때 가장 우수하였다. 젤화제 종류별로 버섯 추출액에 대한 응고 효과는 모든 버섯에 대해 carrageenan이 가장 효과적이었다. 버섯 젤리의 색도는 carrageenan 첨가량에 따라 큰 차이가 없었고, hardness, gumminess, chewiness는 증가하는 경향이었다. 관능은 젤화가 완전히 이루어지면서 hardness가 낮은 carrageenan 0.6% 첨가가 가장 우수하였다.