2018
DOI: 10.1109/access.2018.2873484
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Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

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Cited by 42 publications
(37 citation statements)
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“…We also compared our results with some recently published state-of-the-art (SOTA) MCSR methods, including SSIP [27], SRGR [25], and Zengs model [30]. According to the re- Fig.…”
Section: E Comparing With State-of-the-art Methodsmentioning
confidence: 99%
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“…We also compared our results with some recently published state-of-the-art (SOTA) MCSR methods, including SSIP [27], SRGR [25], and Zengs model [30]. According to the re- Fig.…”
Section: E Comparing With State-of-the-art Methodsmentioning
confidence: 99%
“…Similar to the SISR methods, current MCSR methods can also be categorized into model-based methods and learning-based methods. Examples of model-based methods are based on non-local mean [23], total variation [24], edge gradient [25], shareable information [26], and similarity [27], [28]. The methods using dictionary learning [29] and convolutional neural network [30] are representatives of the learning-based methods.…”
Section: Introductionmentioning
confidence: 99%
“…We develop a gradient-guided residual network (DGGRN) that is based on two intuitions: (1) CNN-based SR methods [12,13] have achieved significant performance advances in MRI super-resolution; and (2) gradient features of the LR image facilitate the recovery of high-frequency details in an HR image [4,28,30,34,36]. DGGRN consists of two subnets.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…To address the over-smoothing issue, the gradient prior is widely applied in reconstruction- [4,27,30] and CNN-based MRI SR methods [33][34][35]. Image gradient provides the exact positions and magnitudes of high-frequency image parts, which are important for improving the accuracy of super-resolution performance.…”
Section: High-frequency Details Recoverymentioning
confidence: 99%
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