Name
Title | ||
|---|---|---|
Lagrangian multipliers and split Bregman methods for minimization problems constrained on Sn-1 |
Abstract | ||
|---|---|---|
The numerical methods of total variation (TV) model for image denoising, especially Rudin-Osher-Fatemi (ROF) model, is widely studied in the literature. However, the S^n^-^1 constrained counterpart is less addressed. The classical gradient descent method for the constrained problem is limited in two aspects: one is the small time step size to ensure stability; the other is that the data must be projected onto S^n^-^1 during evolution since the unit norm constraint is poorly satisfied. In order to avoid these drawbacks, in this paper, we propose two alternative numerical methods based on the Lagrangian multipliers and split Bregman methods. Both algorithms are efficient and easy to implement. A number of experiments demonstrate that the proposed algorithms are quite effective in denoising of data constrained on S^1 or S^2, including general direction data diffusion and chromaticity denoising. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.jvcir.2012.07.002 | J. Visual Communication and Image Representation |
Keywords | DocType | Volume |
lagrangian multiplier,numerical method,general direction data diffusion,minimization problem,classical gradient descent method,alternative numerical method,chromaticity denoising,proposed algorithm,image denoising,split bregman method,small time step size,total variation | Journal | 23 |
Issue | ISSN | Citations |
7 | 1047-3203 | 2 |
PageRank | References | Authors |
0.36 | 26 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Li | 1 | 187 | 9.99 |
| Tieyong Zeng | 2 | 874 | 48.72 |
| Guixu Zhang | 3 | 128 | 25.80 |