Name
Title | ||
|---|---|---|
Fast Augmented Lagrangian Method for Image Smoothing with Hyper-Laplacian Gradient Prior. |
Abstract | ||
|---|---|---|
As a fundamental tool, L-0 gradient smoothing has found a flurry of applications. Inspired by the progress of research on hyper-Laplacian prior, we propose a novel model, corresponding to L-p-norm of gradients, for image smoothing, which can better maintain the general structure, whereas diminishing insignificant texture and impulse noise-like highlights. Algorithmically, we use augmented Lagrangian method (ALM) to efficiently solve the optimization problem. Thanks to the fast convergence rate of ALM, the speed of the proposed method is much faster than the L-0 gradient method. We apply the proposed method to natural image smoothing, cartoon artifacts removal, and tongue image segmentation, and the experimental results validate the performance of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45643-9_2 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Image smoothing,augmented Lagrangian method,hyper-Laplacian gradient prior | Gradient method,Mathematical optimization,Computer science,Algorithm,Image segmentation,Impulse (physics),Smoothing,Augmented Lagrangian method,Rate of convergence,Optimization problem,Laplace operator | Conference |
Volume | ISSN | Citations |
484 | 1865-0929 | 1 |
PageRank | References | Authors |
0.36 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Chen | 1 | 312 | 33.40 |
| Hongzhi Zhang | 2 | 122 | 19.79 |
| Dongwei Ren | 3 | 103 | 12.26 |
| David Zhang | 4 | 1 | 0.36 |
| Wangmeng Zuo | 5 | 3833 | 173.11 |