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Proceedings Paper

Deinterlacing using modular neural network
Author(s): Dong Hun Woo; Il Kyu Eom; Yoo Shin Kim
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Paper Abstract

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

Paper Details

Date Published: 28 May 2004
PDF: 8 pages
Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); doi: 10.1117/12.532554
Show Author Affiliations
Dong Hun Woo, Pusan National Univ. (South Korea)
Miryang National Univ. (South Korea)
Il Kyu Eom, Miryang National Univ. (South Korea)
Yoo Shin Kim, Pusan National Univ. (South Korea)


Published in SPIE Proceedings Vol. 5298:
Image Processing: Algorithms and Systems III
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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