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Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled),...
Based on the assumption that low-resolution (LR) and high-resolution (HR) patch manifolds are locally isometric, the neighbor embedding based super-resolution algorithms try to preserve the local geometry of the patch manifold for the reconstructed HR patch manifold. However, due to “one-to-many” mappings between LR and HR images, the neighborhood relationship of the LR patch manifold can't reflect...
Face image super-resolution (SR) reconstruction is the problem of inducing a high-resolution (HR) face image from a low-resolution (LR) one. Traditional face SR methods are either sensitive to noise, i.e., local patch based technologies, or lacking facial details, i.e., global face reconstruction, thus could not achieve a satisfying result. In order to overcome these problems, we propose in this paper...
By incorporating the prior of positions, position patch based face hallucination methods can produce high-quality results and save computation time. Given a low-resolution face image, the key issue of these methods is how to encode the input low-resolution patch. However, due to stability and accuracy issues, the coding approaches proposed so far are not satisfactory. In this paper, we present a novel...
We explore in this paper efficient algorithmic solutions to single image super-resolution (SR). We propose the GESR, namely Graph Embedding Super-Resolution, to super-resolve a high-resolution (HR) image from a single low-resolution (LR) observation. The basic idea of GESR is to learn a projection matrix mapping the LR image patch to the HR image patch space while preserving the intrinsic geometrical...
In this paper, a novel sparse representation based super-resolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a low resolution (LR) observation via training samples. First, a specific LR and HR over-complete dictionary pair is learned for a certain patch over the patches in all training samples with the same position. Second, K-selection mean constrain is used to...
Super-resolution methods based on sparse easily lead to over-smoothing at the edges of reconstructed image. A novel super-resolution method based on local sparse and global gradient is proposed to solve the problem. First, it represents the input low-resolution (LR) image patches with sparse coefficients and LR over-complete dictionary. Then it maps the coefficients to high resolution (HR) over-complete...
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