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Image Upscaling Using Global Multimodal Priors

  • Hiêp Luong
  • Bart Goossens
  • Wilfried Philips
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4678)

Abstract

This paper introduces a Bayesian restoration method for low-resolution images combined with a geometry-driven smoothness prior and a new global multimodal prior. The multimodal prior is proposed for images that normally just have a few dominant colours. In spite of this, most images contain much more colours due to noise and edge pixels that are part of two or more connected smooth regions. The Maximum A Posteriori estimator is worked out to solve the problem. Experimental results confirm the effectiveness of the proposed global multimodal prior for images with a strong multimodal colour distribution such as cartoons. We also show the visual superiority of our reconstruction scheme to other traditional interpolation and reconstruction methods: noise and compression artifacts are removed very well and our method produces less blur and other annoying artifacts.

Keywords

Document Image Reconstruction Scheme Dominant Colour Image Interpolation Jagged Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hiêp Luong
    • 1
  • Bart Goossens
    • 1
  • Wilfried Philips
    • 1
  1. 1.Ghent University - TELIN - IPI - IBBT, Sint-Pietersnieuwstraat 41, B-9000 GhentBelgium

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