Image deinterlacing using region-based back propagation artificial neural network
- Authors
- Qian, Yurong; Wang, Jin; Jeon, Gwanggil; Jeong, Jechang
- Issue Date
- Jul-2013
- Publisher
- SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
- Keywords
- deinterlacing; back propagation artificial neural network; image format conversion
- Citation
- OPTICAL ENGINEERING, v.52, no.7
- Indexed
- SCIE
SCOPUS
- Journal Title
- OPTICAL ENGINEERING
- Volume
- 52
- Number
- 7
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/162421
- DOI
- 10.1117/1.OE.52.7.073107
- ISSN
- 0091-3286
- 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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