Improved accuracy in regularization models of incompressible flow via adaptive nonlinear filtering

2012 ◽  
Vol 70 (7) ◽  
pp. 805-828 ◽  
Author(s):  
A. L. Bowers ◽  
L. G. Rebholz ◽  
A. Takhirov ◽  
C. Trenchea
Keyword(s):  
Incompressible Flow ◽  
Nonlinear Filtering ◽  
Adaptive Nonlinear Filtering ◽  
Improved Accuracy

scholarly journals Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography

2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Joseph Shtok ◽  
Michael Elad ◽  
Michael Zibulevsky
Keyword(s):  
Computed Tomography ◽  
Nonlinear Filtering ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Image Sharpness ◽  
Image Domain ◽  
Shrinkage Method ◽  
Gradient Based ◽  
Noisy Measurements ◽  
Adaptive Nonlinear Filtering

We propose a direct nonlinear reconstruction algorithm for Computed Tomography (CT), designed to handle low-dose measurements. It involves the filtered back-projection and adaptive nonlinear filtering in both the projection and the image domains. The filter is an extension of the learned shrinkage method by Hel-Or and Shaked to the case of indirect observations. The shrinkage functions are learned using a training set of reference CT images. The optimization is performed with respect to an error functional in the image domain that combines the mean square error with a gradient-based penalty, promoting image sharpness. Our numerical simulations indicate that the proposed algorithm can manage well with noisy measurements, allowing a dose reduction by a factor of 4, while reducing noise and streak artifacts in the FBP reconstruction, comparable to the performance of a statistically based iterative algorithm.

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