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Inverse Halftoning Based on Bayesian Theorem

  • Yun-Fu Liu
  • Jing-Ming Guo
  • Jiann-Der Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

In this work, a method which can generate high quality inverse halftone images from halftone images is proposed. This method uses least-mean-square (LMS) trained filters to establish the relationship between the current processing position and its corresponding neighbor positions in each kind of halftone image. This includes direction binary search (DBS), error diffusion, dot diffusion, and ordered dithering. After which, the support region which is used for features extracting can be obtained by relabeling the LMS-trained filters by order of importance. Two features are used in this work: 1) the probability of black pixel occurrence at each position in the support region, and 2) the probability of mean occurrence which is obtained from all pixels in the support region. According to these data, the probabilities of all possible grayscale values appearance at current processing position can be obtained by Bayesian theorem. Consequently, the final output at this position is the grayscale value with highest probability. Experimental results show that the image quality and memory consumption of the proposed method are superior to Mese-Vaidyanathan’s method.

Keywords

Halftoning inverse halftoning Bayesian theorem halftoning classification error diffusion 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yun-Fu Liu
    • 1
  • Jing-Ming Guo
    • 2
  • Jiann-Der Lee
    • 1
  1. 1.Department of Electrical EngineeringChang Gung UniversityTao-YuanTaiwan
  2. 2.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

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