• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.140-148
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• 2004

Natural images can be classified into edge or flat region. Edges have also various shapes such as long edge, texture and so on. Because the conventional deinterlacing methods commonly use one specific algorithm, they are faced with the difficulty that does not adapt various shapes of images. In this paper, a selective deinterlacing method based on the characteristics of local region of image is proposed. An input image is classified into three regions; flat region, complex edge, long edge. And then for each region, the proper method is assigned according to the characteristic of the local feature. For long edge region, the modified $NEDI(New Edge Directed Interpolation)^{[1]}$ method that interpolates long edge very well is used. The linear $filter^{[2]}$ that enhances high frequency components is used for complex edge, and the bilinear interpolation method is applied to flat region. The proposed method shows improved performance in PSNR and subjective evaluation compared with previous algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.322-331
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• 2003

Advances of high-definition television(HDTV) and personal computers call for the mutual conversion between interlaced signal and progressive signal. Especially, deinterlacing which is known as an interlaced to progressive conversion has been recently required and investigated. In this paper, we propose new deinterlacing algorithm considering sub-pixel motion information. In order to reduce the error of motion estimation, we analyze the effect of inaccurate sub-pixel motion information and model it as zero-mean Gaussian noises added respectively to each low resolution image(field). The error caused by inaccurate motion information is reduced by determining regularization parameter according to the error of motion estimation in each channel. The validity of the proposed algorithm is demonstrated both theoretically and experimentally in this paper.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.62-65
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• 2000

In order to reconstruct a high resolution image, it is important to reconstruct frames from fields. A number of approaches have been developed in making frames. In this paper, we propose a new deinterlacing algorithm based on local motion compensation, which is performed based on statistical property. The proposed algorithm achieves faster processing speed than block matching algorithm and higher resolution than inter-field interpolation. The effectiveness of the proposed algorithm is demonstrated experimentally.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.87-90
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• 1995

In this paper, we propose a new deinterlacing algorithm based on weighted wide vector correlations. This algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but not limited to. By having wide vector correlations, visually annoying artifacts caused by interlacing, such as a serrate line, line crawling, a line flicker, and a large area flicker, can be remarkably reduced, since the use of wide vector correlation increases the detectability of edges in various orientations.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.44-54
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• 1996

In this paper, we propose a new 3-D deinterlacing algorithm based on wide sparse vector correlations and a vertical edge based motion detection algorithm. which is an extension of the deinterlacing algorithm proposed in [10. llJ by the authors. The prooised algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but can also be aplicable to the double scan conversion problesm frequently encountered in ths NTSC systems. By exploiting the edge oriented spatial interpolation based on the wide vector correlations, visually annoying artifiacts caused by interlacing such as a serrate line. line crawling, a line flicker, and a large area flicker can be remarkably reduced since the use of the wide vectors increases the range of the edge orientations that can be detected, and by exploiting sparse vectors correlations the HjW complexity for realizing the algorithm in applications cam be significantly simplified. Simulations are provided indicating thet the proposed algorithm results in a high performance comparable to the performance of the deinterlacing algorithm. based on the wide vector correlations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1250-1257
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• 2003

Generally images are classified into two regions: edge and flat region. While low frequency components are popular in the flat region, high frequency components are quite important in the edge region. Therefore, deinterlacing algorithm that considers the characteristic of each region can be more efficient. In this paper, an image is divided into edge region and flat region by the local variance. And then, for each region, frequency modularized neural network is assigned. Using this structure, each modularized neural network can learn only its region intensively and avoid the complexity of learning caused by the data of different region. Using the local AC data for the input of neural network can prevent the degradation of the performance of teaming due to the average intensity values of image that disturbs the effective learning. The proposed method shows the improved performance compared with previous algorithms in the simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.223-229
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• 2005

In this paper, we proposed an extended edge-based line averaging method for deinterlacing with restricted search range. Conversion from interlaced signal to non-interlaced signal is one of important issues. Conventional deinterlacing algorithms usually utilize edge-based line average algorithm(ELA) within pixel-by-pixel approach. However, it is very sensitive to noise and variation of intensity. To reduce the sensitivity, the proposed method adopts a block-by-block approach and provides reliable direction of edge. Simulation results show that it provides a better performance than other pixel-by-pixel ELA-based methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.233-236
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• 2013

As analog broadcast has replaced digital broadcast and HDTV broadcast has settled as a standard of broadcast system in Korea, most of broadcast in Korea send interlaced signal. For TV uses interlaced scan order, deinterlacing which converts interlaced signal into progressive signal has remain important. In this paper, efficient deinterlacing method considering continuity of edge direction is proposed. Deinterlacing along with the dege of an object is one of the main issue for better visual quality. Thus, an algorithm introduced in this paper consider not only the edge of object but continuity so that a better result is drawn forth. The result shows that the proposed algorithm excels comparing with conventional algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.87-91
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• 2011

In this paper, we propose a new deinterlacing method that converts the interlaced images into the progressive images using a field. Firsr of all, it estimates the direction of edge. If it makes an accurate estimate of the direction, then it interpolates a pixel using ELA(Edge-based Line Average). Otherwise, it estimates the new direction of edge, and then,, it interpolates a pixel using a proposed median filter. From simulation results, it is shown that the proposed method improves both subjective and objective image quality as compared with previous deinterlacing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.89-96
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• 2009

In this paper, we propose an efficient deinterlacing algorithm which is an edge dependent interpolation based on edge direction refinement. The conventional edge dependent interpolation algorithms have a visually better performance than any other Intra-field deinterlacing algorithms. However they are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method detects edge direction of around interpolated pixel and refines the edge direction using weighted median filter. Simulation results have shown the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.