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http://dx.doi.org/10.5573/IEIESPC.2014.3.5.271

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)
Publication Information
IEIE Transactions on Smart Processing and Computing / v.3, no.5, 2014 , pp. 271-274 More about this Journal
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
Multi-frame SR; HF reinforcement; Frequency domain; Spatially weighted bilateral total variance model;
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