• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.334-343
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• 1997

Reference white in color television receiver can be achieved by adjusting the RGB gun current ratio and it is necessary to provide additional gain ratio adjustment for the RGB video signal. Generally, the gun current density profile has a gaussian distribution and the gain-bandwidth product of RGB channel amplifieris constant. Therefore brightness and spatial resolution are changed with variations in reference white of receiver. In this paper, the effect of RGB gun current and channel gain ratios on brightness and resolution of CPT is analyzed. Brightness is increased with the color temperature of referenc white because of Helmholtz-kohlrausch effect. The change in ligh output is more abrupt and spatial resolution is improved with unity current ratio. For more bright and improved ressolution we also present the range of color temperature of reference white for P22 phosphors.

• Korean Journal of Bronchoesophagology
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• v.18 no.2
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• pp.45-48
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• 2012

Background Neoplastic vessels tend to proliferate on the surface of malignant lesions in the aerodigestive tract. So, superficial malignant lesions can be detected earlier by enhancing mucosal vascular clarity. To enhance mucosal vascular clarity on endoscopic image, we developed an image processing algorithm of RGB (red-green-blue) channel substitution image (CSI). Methods Each pixel in original white light image (WLI) has its own value of red, green and blue channel. Various combinations of RGB channel substitution was tried on original WLI. Results To make superficial blood vessels darker than brighter background mucosa, in the CSI algorithm, RGB value in each pixel of WLI is substituted; red value to green one, green value to blue one. There was a good contrast between superficial mucosal vessels and background brighter mucosa in the CSI image. Conclusion By RGB CSI algorithm, WLI could be successfully converted to new images with enhanced mucosal vascular clarity. Using RGB CSI algorithm could provide added vascular visibility on original WLI.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.119-126
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• 2013

In this paper, we propose a chromatic aberration removal algorithm in image capture devices, which considers local properties of the image. Chromatic aberration is generated by the fact that the refractive index of the lens is different for different wavelengths, which produces color artifacts on strong edge due to misalignment of RGB channels. Under the characteristics of the artifacts, the proposed algorithm first estimates the regions with the apparent color artifacts as the neighborhoods of the strong edge. In the regions, the proposed algorithm removes the color artifacts by matching the edges of RGB channels. The widely used conventional methods based on global image warping could not remove the color artifacts of longitudinal chromatic aberration and purple fringing identified by the image sensor, whereas the matching process of the proposed method could reduce them. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1153-1158
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• 2006

The classification of the background and cell areas is very important research area because of the ambiguous boundary. In this paper, the region of cell is extracted from an image of uterine cervical cytodiagnosis using the region growing method that increases the region of interest based on similarity between pixels. Segmented image from background and cell areas is binarized using a threshold value. And then 8-directional tracking algorithm for contour lines is applied to extract the cell area. First, the extracted nucleus is transformed to RGB color that is the original image. Second, the K-means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Third, the Hue information of nucleus is extracted from the HSI models that is the transformation of the clustering values in R, G, and B channels. The backpropagation algorithm is employed to classify and identify the normal or abnormal nucleus.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.163-165
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• 2017

본 논문에서는 카메라 잡음 제거에 딥 러닝 알고리즘을 적용하는 연구를 진행하였다. 합성된 가우시언 잡음에 대하여 좋은 잡음 제거 성능을 보이는 DnCNN(Denoising Convolutional Network)를 이용하여 카메라 잡음을 제거하는 학습과 실험을 진행하였으며, 기준 실험으로는 RGB 색공간의 3채널 모두에 대하여 학습한 신경망(Neural Network)을 사용하였고, 본 논문의 실험에서는 그레이 이미지에 대하여 학습한 신경망을 사용하였다. 신경망의 평가를 위하여 딥 러닝 알고리즘 입력 이미지를 RGB 색공간(RGB Color Space)과 YCbCr 색공간(YCbCr Color Space) 2가지 색공간으로 표현하여 사용하였고, 입력 이미지에 노이즈를 첨가하기 위해 가우시안 노이즈(Gaussian Noise)를 이용하였다. 또한 가우시안 잡음과 다른 성질을 갖는 실제 카메라 잡음에 대해서도 학습과 테스트를 진행하였다.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2399-2402
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• 2003

주위 광원에 화이트 밸런스 된 카메라로 촬영한 피사체의 RGB신호를 D/sub 65/ 광원하의 표준 디스플레이 상에서 느끼는 자극치 L₁M₁S₁값과, 실제 촬영 장소에서 눈이 충분히 주위 조건에 색순응 된 후 피사체에 대해서 느끼는 자극치 L₂M₂S₂값은 다르게 느껴진다. 이는 LMS 시세포의 파장별 감도특성과 카메라의 RGB 칼라 필터의 파장별 감도특성이 다르기 때문이다. 또한 주위 광원의 종류와 밝기에 따른 물리적인 자극 변화에 대해서 카메라의 경우는 RGB 각 채널의 이득이 선형적 변화를 가진다. 그리고 눈의 경우는 LMS 시세포의 감도가 비선형적 특성을 가지기 때문에 색감의 차이를 발생시킨다. 본 논문에서는 촬영시의 주위 조건에서 원 피사체를 직접 볼 때 느끼는 색감을 표준 시환경인 D/sub 65/ 광원하에서 화이트밸런스가 D/sub 65/인 디스플레이를 통해 피사체 이미지를 볼 경우에 동일한 색감을 느끼도록 하는 알고리즘을 제안한다. 제안된 알고리즘을 이용하여 표준 조건하에서 디스플레이 하였을 때 촬영 조건에서의 등색감을 재현할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.616-621
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• 2005

In this paper. digital watermarking algorithm based on human visual system and transform domain is presented. Firstly, original image is separated into RGB thannels, watermark is embedded into the coefficients of DCT so as to consider a contrast sensitivity and texture degrees. In preprocessing, DCT domain based transform is involved and binary image of visually recognizable patterns is utilized as a watermark. Consequently, experimental results showed that proposed algorithm is robust and imperceptibility such destruction attack as JPEG compression.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1181-1187
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• 2009

In this paper, we proposed a method to analyze kind of clouds using a fuzzy reasoning method. In the proposed method, we used the clues that G channel value is dominant from RGB color values in land areas and B channel value is dominant in the sea areas discovered by the analyses of both visible images and infrared images. By these information, R and B channel values are applied to land areas and R and G channel values are applied to the sea areas. Noise areas(areas except cloud areas) are removed from a visible image and an infrared image by a threshold value, and then land areas and the sea areas are discriminated from the noise removed image. Cloud areas are extracted from discriminated areas using R, G, B channel values and a fuzzy reasoning method, and finally kind of clouds is decided by combining same cloud areas included in both the visible image and the infrared image. In comparison with a conventional quantization method, we verified that the performance of cloud analysis by the proposed method is more efficient through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.355-359
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• 2009

본 논문에서는 퍼지 기법을 이용하여 구름의 종류를 분석하는 방법을 제안한다. 본 논문에서는 가시 영상과 적외 영상을 대상으로 육지 영역은 RGB 컬러 정보 중에 G 채널 값의 수치가 높고, 바다영역에서는 B 채널 값의 수치가 높다는 정보를 이용한다. 이 정보를 이용하여 육지 영역에서는 R과 B 채널 값을 적용하고, 바다 영역에서는 R과 G 채널 값을 적용한다. 가시 영상과 적외 영상에서 임계치를 적용하여 잡음(구름 이외의 영역)을 제거하고, 잡음을 제거한 영상에서 육지 영역과 바다 영역을 구분한 후, 각 R, G, B 채널 정보를 퍼지 기법에 적용하여 구름 영역을 판별한다. 그리고 가시영상과 적외 영상에 모두 포함된 구름 영역에 대해서는 두 영상을 합성하여 구름을 판별한다. 제안된 기법을 구름 분류에 적용한 결과, 제안된 방법이 기존의 양자화를 적용한 방법보다 구름의 분류 성능이 개선된 것을 확인하였다.