Name
Abstract | ||
|---|---|---|
In this paper, we develop a new wavelet domain statistical model for the removal of stationary noise in images. The new model is a combination of local linear projections onto bases of Principal Components, that perform a dimension reduction of the spatial neighbourhood, while avoiding the "curse of dimensionality". The models obtained after projection consist of a low dimensional Gaussian Scale Mixtures with a reduced number of parameters. The results show that this technique yields a significant improvement in denoising performance when using larger spatial windows, especially on images with highly structured patterns, like textures. |
Year | Venue | Keywords |
|---|---|---|
2007 | ACIVS | new wavelet domain,dimension reduction,denoising performance,gaussian scale mixtures,principal components,statistical model,new model,principal component,larger spatial windows,noise removal,spatial neighbourhood,local linear projection,curse of dimensionality |
Field | DocType | Volume |
Noise reduction,Dimensionality reduction,Pattern recognition,Computer science,Minimum mean square error,Curse of dimensionality,Artificial intelligence,Statistical model,Noise removal,Principal component analysis,Wavelet | Conference | 4678 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-74606-4 | 1 |
PageRank | References | Authors |
0.36 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bart Goossens | 1 | 220 | 25.94 |
| Aleksandra Pizurica | 2 | 1238 | 102.29 |
| Wilfried Philips | 3 | 115 | 10.61 |