Search Results for author:
Found 3 papers, 0 papers with code
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction
Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality.
Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions.
A convergence proof of the split Bregman method for regularized least-squares problems
According to our analysis, we can show that the two-split ADMM algorithm can be faster than the SB method if the AL penalty parameter of the SB method is suboptimal.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
