Share Email Print

Optical Engineering

Image deinterlacing using region-based back propagation artificial neural network
Author(s): Yurong Qian; Jin Wang; Gwanggil Jeon; Jechang Jeong
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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 8−16−1 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)

© SPIE. Terms of Use
Back to Top