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Optical Engineering

Image deinterlacing using region-based back propagation artificial neural network
Author(s): Yurong Qian; Jin Wang; Gwanggil Jeon; Jechang Jeong
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Paper Abstract

A back propagation artificial neural network (BP-ANN) has good self-learning, self-adaptation and generalization abilities, which we intend to use to interpolate an image. The interpolated pixels are classified into two regions, each region corresponding to one BP-ANN. In order to optimize the structure of the BP-ANN and the process of deinterlacing, three experiments were performed to test the architecture and parameters of region-based BP-ANN. The experimental results show that the proposed algorithm with an 8161 structure provides the best balance between time consumption and visual quality. Compared to the other six advanced deinterlacing algorithms, our region-based BP-ANN method provides about an average of 0.14 to 0.64 dB higher peak signal-to-noise-ratio while maintaining high efficiency.

Paper Details

Date Published: 15 July 2013
PDF: 9 pages
Opt. Eng. 52(7) 073107 doi: 10.1117/1.OE.52.7.073107
Published in: Optical Engineering Volume 52, Issue 7
Show Author Affiliations
Yurong Qian, Xinjiang Univ. (China)
Jin Wang, Hanyang Univ. (Korea, Republic of)
Gwanggil Jeon, Univ. of Incheon (Korea, Republic of)
Jechang Jeong, Hanyang Univ. (Korea, Republic of)

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