Multi-Frame Super-Resolution of High Frequency with Spatially Weighted Bilateral Total Variance Regularization
Oh-Young Lee, Sae‐Jin Park, Jae-Woo Kim, Jong‐Ok Kim · IEIE Transactions on Smart Processing and Computing · 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.