Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
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..., total variation (TV) [17]–[19], discrete cosine transforms [20]–[22] and discrete wavelet transforms [23]–[25]....
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"Image reconstruction of compressed ..." refers background or methods or result in this paper
...Both methods significantly improve the image reconstruction over the predefined basis method (Ning et al., 2013; Qu et al., 2012; Ravishankar and Bresler, 2011)....
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...For example, the geometric edge of a patch has been applied to train the adaptively sparse representations (Ning et al., 2013; Qu et al., 2012)....
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...…on K-SVD decomposition, wavelet tree-structured MRI (WaTMRI) (Chen and Huang, 2014), which enforces the sparsity based on quadtree structures of wavelet coefficients, and PBDW (Qu et al., 2012), which is a patch-based directional wavelet transform that makes use of geometric information in patches....
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...The overlapping factor is defined as: 2c lρ= (Qu et al., 2012), where ρ is the aforementioned patch size....
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...The image reconstruction may become unsatisfactory when the data are highly undersampled because of the insufficiently sparse representations (Qu et al., 2012; Ravishankar and Bresler, 2011)....
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"Image reconstruction of compressed ..." refers background in this paper
...In CS-MRI, finding an optimally sparse representation for magnetic resonance (MR) images is important because the reconstruction error is usually lower if the image representation is sparser (Qu et al., 2012; Qu et al., 2014; Qu et al., 2010; Ravishankar and Bresler, 2011)....
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150 citations
"Image reconstruction of compressed ..." refers methods in this paper
...…methods have attracted considerable interest in CS-MRI because adaptively sparse representations can be trained with easy manipulations on patches (Akcakaya et al., 2011; Akcakaya et al., 2014; Maggioni et al., 2013; Ning et al., 2013; Qu et al., 2012; Qu et al., 2014; Ravishankar and Bresler,…...
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125 citations
"Image reconstruction of compressed ..." refers background in this paper
...…CS-MRI because adaptively sparse representations can be trained with easy manipulations on patches (Akcakaya et al., 2011; Akcakaya et al., 2014; Maggioni et al., 2013; Ning et al., 2013; Qu et al., 2012; Qu et al., 2014; Ravishankar and Bresler, 2011; Wang and Ying, 2014; Yue et al., 4 / 27 2014)....
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