Name
Abstract | ||
|---|---|---|
In this paper, we proposed a new splitting algorithm for MR image reconstruction from random variable density sample. Due to the sparsity in the transform domain and piecewise smoothness in the spatial domain of MR images, the reconstruction can be obtained by performing total variation and wavelet L-1 regularization optimization. By introduce an auxiliary variable, we derive a new quadratic majorizing function for data fitting term in the objective function. Alternative minimization approach is applied to find the minimizer of the objective function. For the auxiliary variable, the minimum has a closed form solution, and for the original variable, the minimum is a proximity operator of the hybrid regularizers. We develop an efficient algorithm to compute the proximity operator. We compare the proposed algorithm with gradient methods in term of signal-to-noise ratio. Numerical results demonstrate that the proposed algorithm is very efficient and outperforms that of gradient methods. |
Year | DOI | Venue |
|---|---|---|
2012 | null | COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS III |
Field | DocType | Volume |
Iterative reconstruction,Computer vision,Computer science,Sampling (statistics),Artificial intelligence | Conference | null |
Issue | ISSN | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi-Ying Cao | 1 | 3 | 0.77 |
| You-Wei Wen | 2 | 353 | 18.93 |