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Multi-Frame Super-Resolution of High Frequency with Spatially Weighted Bilateral Total Variance Regularization

  • Lee, Oh-Young (School of Electrical Engineering, Korea University) ;
  • Park, Sae-Jin (School of Electrical Engineering, Korea University) ;
  • Kim, Jae-Woo (School of Electrical Engineering, Korea University) ;
  • Kim, Jong-Ok (School of Electrical Engineering, Korea University)
  • Received : 2014.01.15
  • Accepted : 2014.07.28
  • Published : 2014.10.31

Abstract

Bayesian based Multi-Frame Super-Resolution (MF-SR) has been used as a popular and effective SR model. On the other hand, the texture region is not reconstructed sufficiently because it works on the spatial domain. In this study, the MF-SR method was extended to operate on the frequency domain to improve HF information as much as possible. For this, a spatially weighted bilateral total variation model was proposed as a regularization term for a Bayesian estimation. The experimental results showed that the proposed method can recover the texture region more realistically with reduced noise, compared to conventional methods.

Keywords

Acknowledgement

Supported by : NIPA (National IT Industry Promotion Agency)

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