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Image self-similarity property is important to super-resolution reconstruction. However, how to effectively exploit the self-similarity information to reconstruct an underlying high-resolution image is still a challenging problem. The authors propose a novel model for solving the single image upsampling problem with the self-similarity property. First, the authors construct a statistical prior that...
This paper proposes a novel variational dimension frequency domain method for feature-adaptive mesh Representation. The eigen functions given by the eigen decomposition of the Laplace-Beltrami operator are used to define Fourier like function basis to project the actual geometry of the mesh into the spectral space because of their geometry aware and orthogonal. But, low-pass filters based on Fourier-like...
In this paper, we present a simple and novel approach for solving single image super-resolution (SISR) without external data set. Based on a variant of gaussian process regression (GPR), SISR is formulated as a multi-task regression problem in which each learning task refers to estimation of the regression function for each image patch. Unlike conventional methods, which need to specify the form of...
This paper presents an image hierarchical super-resolution (SR) method with adaptive dictionaries, based on signal sparse representation. It can not only improve image detail quality but also reduce computational cost. Research on the human visual system suggests that our eyes are mainly sensitive to high-frequency contents. Inspired by this observation, we implemented a hierarchical process where...
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