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Interpolation scheme based on the Bayes classifier

[+] Author Affiliations
Sang-Jun Park

Vision Sensor Engineering Team, Hyundai Mobis Co., Ltd., 17-2 Mabook-ro, Giheung-gu, Yongin-si, Gyunggi-do, Republic of Korea

Gwanggil Jeon

Incheon National University, Department of Embedded Systems Engineering, 12-1 Songdo-dong, Yeonsu-gu, Incheon, Republic of Korea

Jiaji Wu

Xidian University, Institute of Intelligent Information Processing, Ministry of Education of China, Key Laboratory of Intelligent Perception and Image Understanding, Xi’an, Shaanxi, China

Jechang Jeong

Hanyang University, Department of Electronics and Computer Engineering, 222 Wangsimni-ro, Seongdong-gu, Seoul, Republic of Korea

J. Electron. Imaging. 22(2), 023003 (Apr 10, 2013). doi:10.1117/1.JEI.22.2.023003
History: Received December 8, 2012; Revised March 4, 2013; Accepted March 19, 2013
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Abstract.  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.

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© 2013 SPIE and IS&T

Citation

Sang-Jun Park ; Gwanggil Jeon ; Jiaji Wu and Jechang Jeong
"Interpolation scheme based on the Bayes classifier", J. Electron. Imaging. 22(2), 023003 (Apr 10, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.023003


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