Name
Abstract | ||
|---|---|---|
Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIP.2012.2205007 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image representation,mean absolute deviation error,additive denoising problem,logarithmic transformation,image processing problem,dictionary,maximum likelihood estimation,image denoising,image recovery,maximum a posteriori formulation,denoising,peak signal-to-noise ratio,variational model,multiplicative noise removal,multiplicative denoising problem,sparse representation,multiplicative noise,learned dictionary,visual quality | Noise reduction,Multiplicative function,Data transformation (statistics),Pattern recognition,Non-local means,Sparse approximation,Image processing,Artificial intelligence,Maximum a posteriori estimation,Mathematics,Multiplicative noise | Journal |
Volume | Issue | ISSN |
21 | 11 | 1941-0042 |
Citations | PageRank | References |
28 | 0.85 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Mei Huang | 1 | 258 | 11.83 |
| Lionel Moisan | 2 | 599 | 38.20 |
| Ng Michael | 3 | 4231 | 311.70 |
| Tieyong Zeng | 4 | 874 | 48.72 |