Regular Articles

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
Text Size: A A A

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.

Figures in this Article
© 2013 SPIE and IS&T


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). ;

Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via