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Image superresolution as sparse representation of raw image patches (2008)

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by Jianchao Yang , John Wright , Yi Ma , Thomas Huang
Citations:135 - 19 self
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BibTeX

@MISC{Yang08imagesuperresolution,
    author = {Jianchao Yang and John Wright and Yi Ma and Thomas Huang},
    title = {Image superresolution as sparse representation of raw image patches },
    year = {2008}
}

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Abstract

This paper addresses the problem of generating a superresolution (SR) image from a single low-resolution input image. We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as downsampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signalatoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the downsampled 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.

Keyphrases

sparse representation    image superresolution    raw image patch    compressed sensing    mild condition    good dictionary    high-resolution image    single low-resolution input image    input image    prototype signalatoms    small set    computed representation    recovered high-resolution image    raw patch    downsampled signal    over-complete dictionary    downsampled version    ill-posed super-resolution problem    sr method    low-resolution image    similar statistical nature   

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