Wavelet-content-adaptive BP neural network-based deinterlacing algorithm
- Title
- Wavelet-content-adaptive BP neural network-based deinterlacing algorithm
- Author
- 정제창
- Keywords
- Deinterlacing; BP neural network; Pixel classification
- Issue Date
- 2018-03
- Publisher
- SPRINGER
- Citation
- SOFT COMPUTING, v. 22, no. 5, page. 1595-1601
- Abstract
- In this paper, we introduce an intra-field deinterlacing algorithm based on a wavelet-content-adaptive back propagation (BP) neural network (BP-NN) using pixel classification. During interpolation, there is an issue of different image features having completely different properties, such as smooth regions, edges, and textures. We use the wavelet transform to divide the images into several pieces with different properties. Then, each piece has similar image features and each one is assigned to one neural network. The BP-NN-based deinterlacing algorithm can reduce blurring by recovering the missing pixels via a learning process. Compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise ratio and visual quality while maintaining high efficiency.
- URI
- https://link.springer.com/article/10.1007%2Fs00500-017-2968-xhttps://repository.hanyang.ac.kr/handle/20.500.11754/117996
- ISSN
- 1432-7643; 1433-7479
- DOI
- 10.1007/s00500-017-2968-x
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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