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However, CT has potential risk for high radiation dose and in addition to new strategy of reducing dose such as development of image reconstruction algorithm in sparse view conditions.
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Conventional image reconstruction algorithms, such as filtered back projection (FBP), may produce severe artifacts in images reconstructed from sparse-view projections.
New reconstruction methods for sparse-view CT and interior CT will be an alternative to dose reduction.
The first strategy is the sparse-view CT and the second one is the interior CT [ 60].
The main benefit of our proposed method in sparse projection view is that it provides excellent image performance with efficient reconstruction in sinogram domain and noise reduction ratio.
We have recently proposed algorithms for image reconstruction from fan-and cone-beam data collected at highly sparse angular views through minimization of the total-variation (TV) of the image subject to the condition that the estimated data are consistent with the measured data.
To address this issue, we propose a multi-view sparse subspace learning method called Multi-view Sparsity Preserving Projection (MvSPP) in this paper.
Then, the multiple-view sparse representation could be regarded as a single-view problem and solved by SRC.
MvSPP seeks to find a set of linear transforms to project multi-view features into one common low-dimensional subspace where multi-view sparse reconstructive weights are preserved as much as possible.
In much of southern Africa the dry season, from May to November, is peak safari time, with roads open, wildlife coming to water and sparse vegetation making viewing easier.
In accord with SR2MCC, a classifier, called multi-view sparse representation based classifier (MvSRC) is further developed.
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