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- W3005678761 abstract "The low-resolution image is viewed as down sampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the down sampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high resolution image is competitive or even superior in quality to images produced by other SR methods." @default.
- W3005678761 created "2020-02-24" @default.
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- W3005678761 date "2019-12-01" @default.
- W3005678761 modified "2023-09-23" @default.
- W3005678761 title "An Efficient Approach for Image Resolution Enhancement Using Multi Step Magnification Method" @default.
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- W3005678761 doi "https://doi.org/10.1109/wocn45266.2019.8995110" @default.
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