2018
DOI: 10.1155/2018/9262847
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Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting

Abstract: Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain. However, the nature of the super-resolution is to restore high-frequency components in frequency domain rather than upsampling in signal domain. In that sense, there is a close relationship between super-resolution of an image and extrapolation of the spectrum. In this study, we propose a novel framework for supe… Show more

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Cited by 4 publications
(10 citation statements)
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References 50 publications
(73 reference statements)
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“…The first application of SR in MRI was proposed by Peled et al, where multiples of spatially shifted, single‐shot, diffusion‐weighted brain images were fused to generate a new image with improved resolution and finer detail . Since then, various advanced SR techniques established in MRI have offered the possibility to efficiently improve the image resolution and increase the diagnostic potential …”
Section: Introductionmentioning
confidence: 99%
See 3 more Smart Citations
“…The first application of SR in MRI was proposed by Peled et al, where multiples of spatially shifted, single‐shot, diffusion‐weighted brain images were fused to generate a new image with improved resolution and finer detail . Since then, various advanced SR techniques established in MRI have offered the possibility to efficiently improve the image resolution and increase the diagnostic potential …”
Section: Introductionmentioning
confidence: 99%
“…To resolve such a persistent problem in MRI, the image postprocessing technique known as super resolution (SR) may be utilized to significantly improve the spatial resolution of MRI without changing hardware or scanning components. 2,[17][18][19][20][21][22][23][24] The aim of SR reconstruction is to reconstruct HR images from a single or a set of low-resolution (LR) images to improve the visibility of, or recover, image details. The first application of SR in MRI was proposed by Peled et al, where multiples of spatially shifted, single-shot, diffusion-weighted brain images were fused to generate a new image with improved resolution and finer detail.…”
Section: Introductionmentioning
confidence: 99%
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“…Mathematical models that assume smoothness play an important role in a wide range of fields such as signal processing and pattern recognition. Smooth mathematical models are directly useful for noise reduction and interpolation of corrupted signals, and have many applications in time-series restoration [29], image restoration [12,13,21,24,35,36,40,53], color-image restoration [14,15,22,23,41,43], MR image reconstruction [19,20,30], dynamic PET reconstruction [17,18,45], and hyper spectral image restoration [16,48].…”
Section: Introductionmentioning
confidence: 99%