10 April 2013 Interpolation scheme based on the Bayes classifier
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Our purpose is to present an intrafield deinterlacing method using the Bayes classifier. The conventional intrafield deinterlacing methods interpolate the pixel along the local edge direction, but they yield interpolation errors when the local edge direction is determined to be wrong. On the basis of the Bayes classifier, the proposed algorithm performs region-based deinterlacing. The proposed algorithm utilizes an input feature vector that includes five directional correlations, which are used to extract the characteristics of the local region, to classify the local region. After the classification of the local region, one of the three simple interpolation methods, which possesses the highest probability to be used among the three, is chosen for the corresponding local region. In addition, we categorized the range of the feature vector to reduce the computational complexity. Simulation results show that the proposed Bayes classifier-based deinterlacing method minimizes interpolation errors. Compared to the traditional deinterlacing methods and Wiener filter-based interpolation method, the proposed method improves the subjective quality of the reconstructed image, and maintains a higher peak signal-to-noise ratio level.
© 2013 SPIE and IS&T
Sang-Jun Park, Sang-Jun Park, Gwanggil Jeon, Gwanggil Jeon, Jiaji Wu, Jiaji Wu, Jechang Jeong, Jechang Jeong, } "Interpolation scheme based on the Bayes classifier," Journal of Electronic Imaging 22(2), 023003 (10 April 2013). https://doi.org/10.1117/1.JEI.22.2.023003 . Submission:

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