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- W2043350360 abstract "In this paper, a novel method for solving single image super-resolution problem is proposed. Objective is to recover a high resolution version of the given low resolution image. This method takes into account, a popular method of dimensionality reduction. It is believed that, small image patches in the low-resolution and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. In locally linear embedding, focus is on reconstruction of a feature vector for a given patch using its neighbors in the feature space. This feature vector is used for the super resolution." @default.
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- W2043350360 date "2011-10-01" @default.
- W2043350360 modified "2023-09-25" @default.
- W2043350360 title "Super-Resolution Using Manifold Learning" @default.
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- W2043350360 doi "https://doi.org/10.1109/cicn.2011.154" @default.
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