Department of Mathematics
Alternating algorithms for total variation image reconstruction from random projections
Total variation (TV) regularization is popular in image reconstruction due to its edgepreserving property. In this paper, we extend the alternating minimization algorithm recently proposed in  to the case of recovering images from random projections. Specifically, we propose to solve the TV regularized least squares problem by alternating minimization algorithms based on the classical quadratic penalty technique and alternating minimization of the augmented Lagrangian function. The per-iteration cost of the proposed algorithms is dominated by two matrixvector multiplications and two fast Fourier transforms. Convergence results, including finite convergence of certain variables and q-linear convergence rate, are established for the quadratic penalty method. Furthermore, we compare numerically the new algorithms with some state-of-the-art algorithms. Our experimental results indicate that the new algorithms are stable, efficient and competitive with the compared ones. © 2012 American Institute of Mathematical Sciences.
Alternating direction method, Image reconstruction, Quadratic penalty, Random projection, Total variation
Source Publication Title
Inverse Problems and Imaging
American Institute of Mathematical Sciences
Link to Publisher's Edition
Xiao, Yunhai, Junfeng Yang, and Xiaoming Yuan. "Alternating algorithms for total variation image reconstruction from random projections." Inverse Problems and Imaging 6.3 (2012): 547-563.