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Frequency Mudularized Deinterlacing Using Neural Network  

우동헌 (부산대학교 전자공학과 지능정보처리 연구실)
엄일규 (밀양대학교 정보통신공학과)
김유신 (부산대학교 컴퓨터 및 정보통신연구소)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.28, no.12C, 2003 , pp. 1250-1257 More about this Journal
Abstract
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.
Keywords
deinterlacing; neural network; modular;
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