• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.92-100
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• 2006

In imaging devices such as digital cameras using a single image sensor, captured images are the sub-sampled images comprised of the pixels that have only one of the three primary colors per a pixel. This images should be restored to the color images through an image processing referred as color interpolation. In this paper, we derive relation between the average of the data from CFA image sensor and the average of each color channel data. By using this relation, a new efficient method for color interpolation is proposed. Also, in order to reduce the zipper effect in a restored image, missing luminance values are interpolated along any edges in the captured image. On the other hand, for the chrominance channel interpolation, we average difference between a chrominance value and a luminance value in a local area, and this average value is added to the pixel value of the interpolated location. The proposed method has been compared with several previous methods, and our experimental results show the better results than the other methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.86-94
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• 2010

In this paper, we propose spatially adaptive color demosaicing of noisy Bayer data. When sensor noises are not considered in demosaicing, they may degrade result image. In order to obtain high resolution image, sensor noises are considered in the color demosaicing step. We identify flat, edge and pattern regions at each pixel location to improve the performance of the algorithm and to reduce complexity. Based on the pre-classified regions, the demosaicing of the G channel is performed using the local statistics to reduce the interpolation error. The sensor noise is simultaneously removed by a modified version of non-local mean filter in the green and in the color difference domain. The R and B channels are interpolated easily using fully interpolated and denoised G and color difference values. Experimental results show that the proposed method achieves a significant improvement in terms of visual and numerical criteria, when compared to conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.1-10
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• 2010

In this paper, we propose a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is considered during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. We propose a modified edge-based AWB method that used a pre-defined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. The proposed method shows significant improvements in terms of visual and numerical criteria when compared to conventional methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.105-110
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• 2016

To reduce the cost and volume of a digital camera, a subsampled color filter array(CFA) image is used and demosaicking is applied to estimate the missing color values. However, aliasing, the overlaps of signals in the frequency domain, occurs when signals are subsampled. This causes aliasing artifacts such as false colors and zipper effects in demosaicking processes. In this paper, the algorithm estimating high-quality color images by removing aliasing artifacts in them is proposed. The aliasing region map is estimated using the sub-sampled signals of the CFA image. By using the aliasing region map and the estimated luminance image, the least squares problem of the observation models is designed and aliasing artifacts are eliminated. The experiments demonstrate that the proposed algorithm restores color images without aliasing artifacts.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.561-565
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• 2011

This paper presents an imaging system for reconstruction of enhanced color images using the conventional Bayer CFA. By extracting various colors such as RGBCY from two sequential images which consist of a image by broadband G channel lens filter and the other image captured without one, the proposed color image reconstruction system can reduce the computational complexity for demosaicking and make high resolution color information without aliasing artifacts. Because the proposed system uses the common Bayer CFA image sensor, fabricating a new type of CFA is not necessary for obtaining a multi-spectral image, which can be easily extensible for applications of multi-spectral imaging. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing demosaicking methods, the proposed camera system showed the significant improvements in the sense of color resolution.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.174-188
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• 2004

In the life environment changed with not only the quality and the price of the products but also the material abundance, it is the most crucial factor for the strategy of product sales to investigate consumer's sensibility and preference degree. In this perspective, it is necessary to design and merchandise the products in cope with each consumer's sensibility and needs as well as its functional aspects. In this paper, we propose the Fashion Design Recommender Agent System (FDRAS-pro) for textile design applying collaborative filtering personalization technique as one of the methods of material development centered on consumer's sensibility and preference. For a collaborative filtering system based on textile, Representative-Attribute Neighborhood is adopted to determine the number or neighbors that will be used for preferences estimation. Pearson's Correlation Coefficient is used to calculate similarity weights among users. We build a database founded on the sensibility adjectives to develop textile designs by extracting the representative sensibility adjectives from users' sensibility and preferences about textile designs. FDRAS-pro recommends textile designs to a customer who has a similar propensity about textile. To investigate the sensibility and emotion according to the effect of design factors, fertile designs were analyzed in terms of 9 design factors, such as, motif source, motif-background ratio, motif variation, motif interpretation, motif arrangement, motif articulation, hue contrast, value contrast, chroma contrast. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our system.