Name
Abstract | ||
|---|---|---|
Processing and classification of color Natural Stochastic Textures (NST) are of importance in various facets of image restoration, enhancement and pattern recognition. Existing denoising and deblurring algorithms produce over-smoothed images with sharp edges, but do not restore the fine textural color details. A recently proposed color-NST model, endowed with a small number of parameters, is extended and used for deblurring and denoising via a linear maximum-a-posteriori (MAP) scheme. The restored images exhibit better textural details than those recovered by other algorithms. Orientation and coherence-based features are combined with the color-NST model for classification, showing improvement over algorithms implementing only isotropic and color-based features. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | Computer vision,Image gradient,Deblurring,Color histogram,Pattern recognition,Feature (computer vision),Non-local means,Binary image,Artificial intelligence,Image restoration,Mathematics,Color image |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
19 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ido Zachevsky | 1 | 20 | 2.75 |
| Yehoshua Y. Zeevi | 2 | 610 | 248.69 |