Frequency Modularized Deinterlacing Using Neural Network 


Vol. 28,  No. 12, pp. 1250-1257, Dec.  2003


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  Abstract

Generally images are classified into two regions: edge and flat region. While low frequency components are popular in the flat region, high frequency componets 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 learning 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.

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  Cite this article

[IEEE Style]

D. Woo, I. Eom, Y. Kim, "Frequency Modularized Deinterlacing Using Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 12, pp. 1250-1257, 2003. DOI: .

[ACM Style]

Dong-Hun Woo, Il-Kyu Eom, and Yoo-Shin Kim. 2003. Frequency Modularized Deinterlacing Using Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 28, 12, (2003), 1250-1257. DOI: .

[KICS Style]

Dong-Hun Woo, Il-Kyu Eom, Yoo-Shin Kim, "Frequency Modularized Deinterlacing Using Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 12, pp. 1250-1257, 12. 2003.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880