Image deinterlacing using region-based back propagation artificial neural network

Yurong Qian; Jin Wang; Gwanggil Jeon; Jechang Jeong

doi:10.1117/1.OE.52.7.073107

15 July 2013 Image deinterlacing using region-based back propagation artificial neural network

Yurong Qian, Jin Wang, Gwanggil Jeon, Jechang Jeong

Author Affiliations +

Optical Engineering, Vol. 52, Issue 7, 073107 (July 2013). https://doi.org/10.1117/1.OE.52.7.073107

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.

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Yurong Qian, Jin Wang, Gwanggil Jeon, and Jechang Jeong "Image deinterlacing using region-based back propagation artificial neural network," Optical Engineering 52(7), 073107 (15 July 2013). https://doi.org/10.1117/1.OE.52.7.073107

Published: 15 July 2013

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