Name
Abstract | ||
|---|---|---|
Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1137/080712593 | SIAM J. Imaging Sciences |
Keywords | Field | DocType |
observed image,image edge,orginal image,multiplicative noise removal problem,new total variation method,multiplicative noise removal,convex objective function,additive noise removal problem,objective function,original image,total variation,multiplicative noise,convex function | Convergence (routing),Value noise,Mathematical optimization,Convex function,Total variation denoising,Gaussian noise,Noise removal,Multiplicative noise,Mathematics,Gradient noise | Journal |
Volume | Issue | ISSN |
2 | 1 | 1936-4954 |
Citations | PageRank | References |
69 | 2.21 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Mei Huang | 1 | 258 | 11.83 |
| Ng Michael | 2 | 4231 | 311.70 |
| You-Wei Wen | 3 | 353 | 18.93 |