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Showing 1 to 4 of 4 for “"Total Variation Minimization"”.
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Selection of Step Size for Total Variation Minimization in CT
<p>Medical image reconstruction by total variation minimization is a newly developed area in computed tomography (CT). In compressed sensing literature, it hasbeen shown that signals with sparse representations in an orthonormal basis may be reconstructed via l1-minimization. Furthermore, if an …
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Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing
… relied on methods such as finite difference, L1 total variation minimization, or Savitzky-Golay filtering. Kalman smoothing, a classical approach for data assimilation with known noise statistics, has recently been incorporated into pysindy alongside hyperparameter optimization to enhance data …
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Application and Development of Algebraic Reconstruction Algorithms for High Dose Rate Brachytherapy Gel Dosimetry with Optical Computed Tomography Readout
… using the Ordered Subsets Convex algorithm with Total Variation minimization regularization (OSC-TV) instead of the FDK algorithm to suppress the artifacts. Three variants of the OSC-TV algorithm were also developed. These variants introduced reconstructing using flood field images (OSC-TV-FF), …
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Development of Novel Cardiac CT Methods
… ROI using interior tomography. The ability of a total-variation minimization based algorithm was then demonstrated with a limited number of projections. Since the circular cone-beam trajectory does not satisfy the data completeness condition, which results in artifacts, we extended it to the …