• Title/Summary/Keyword: Spatially weighted bilateral total variance model

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

  • Lee, Oh-Young;Park, Sae-Jin;Kim, Jae-Woo;Kim, Jong-Ok
    • IEIE Transactions on Smart Processing and Computing
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    • v.3 no.5
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    • pp.271-274
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    • 2014
  • 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.