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Journal of Electronic Imaging

Deinterlacing algorithm using gradient-regularized modular neural networks
Author(s): Hao Zhang; Ruolin Wang; Wenjiang Liu; Mengtian Rong
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Paper Abstract

An intrafield deinterlacing algorithm based on gradient-regularized modular neural networks is proposed. The proposed method defines six gradient regularization terms for every missing pixel. Different modular neural networks are selectively used according to the gradient of the pixel to be interpolated. With the statistics of the six gradient regularization terms, a more robust output is generated by modular neural networks. When compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise-ratio while achieving better subjective quality.

Paper Details

Date Published: 4 February 2014
PDF: 6 pages
J. Electron. Imaging. 23(1) 013014 doi: 10.1117/1.JEI.23.1.013014
Published in: Journal of Electronic Imaging Volume 23, Issue 1
Show Author Affiliations
Hao Zhang, Shanghai Jiao Tong Univ. (China)
Ruolin Wang, Shanghai Jiao Tong Univ. (China)
Wenjiang Liu, Shanghai Jiao Tong Univ. (China)
Mengtian Rong, Shanghai Jiao Tong Univ. (China)


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