2013
DOI: 10.1016/j.media.2012.09.003
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Single-image super-resolution of brain MR images using overcomplete dictionaries

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Cited by 163 publications
(115 citation statements)
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References 38 publications
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“…For instance, [2,3] use example patches from HR images to super-resolve scalar MR and DW images respectively, with an explicitly defined generative model relating a HR patch to a LR patch and carefully crafted regularisation. Another generative approach is the sparse-representation methods [4,5], which construct a coupled library of HR and LR images from training data and solve the SR problem through projection onto it. Image quality transfer (IQT) [6] is a general quality-enhancement framework based on patch regression, which shows great promise in SR of DT images and requires no special acquisition, so is applicable to large varieties of existing data.…”
Section: Introductionmentioning
confidence: 99%
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“…For instance, [2,3] use example patches from HR images to super-resolve scalar MR and DW images respectively, with an explicitly defined generative model relating a HR patch to a LR patch and carefully crafted regularisation. Another generative approach is the sparse-representation methods [4,5], which construct a coupled library of HR and LR images from training data and solve the SR problem through projection onto it. Image quality transfer (IQT) [6] is a general quality-enhancement framework based on patch regression, which shows great promise in SR of DT images and requires no special acquisition, so is applicable to large varieties of existing data.…”
Section: Introductionmentioning
confidence: 99%
“…We incorporate Bayesian inference into the framework and name the new method Bayesian IQT (BIQT). Although many SR methods [2][3][4][5] can be cast as maximum a posteriori (MAP) optimisation problems, the dimensionality or complexity of the posterior distribution make the computation of uncertainty very expensive. In contrast, the random forest implementation of the original IQT is amenable to uncertainty estimation thanks to the simple linear model at each leaf node, but the current approach computes maximum likelihood (ML) solution.…”
Section: Introductionmentioning
confidence: 99%
“…By using training data that covers the whole cardiac cycle, we can account for the variation of motion likely in the test image. A Gaussian kernel, with full width half max (FWHM) equal to the slice thickness, is used to blur in the slice-select direction only, in order to simulate the acquisition process [9]. The resulting LR images are then up sampled using bi-cubic interpolation back to the original size, giving.…”
Section: Trainingmentioning
confidence: 99%
“…The PANO operator incorporated prior information learned from under-sampled data or another contrast image, which led to optimized sparse representation of images to be reconstructed. Other medical approaches, for example [19], generate overcomplete dictionaries to enhance the quality of Magnetic Resonance (MR) images. This couples high and low frequency information, so an HR version of an LR brain MR image is generated.…”
Section: Introductionmentioning
confidence: 99%