Name
Abstract | ||
|---|---|---|
In this paper, we are interested in preserving the sharp transitions and edges present inside the image. Image denoising by means of wavelet transforms has been an active research topic for many years. In this work, we used Lipschitz exponents based on wavelet transform to performs edge preservation by identifying these transitions. The smoothing part was performed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain with out prior modeling of either the image or noise statistics. The method tries to find the the best compromise between the data and the smoothing criteria based on the type of the transition present. The method has been compared with the shrinkage approach, Wiener filter and Non Local- means algorithm as well. Experimental results showed that the proposed method gives better signal to noise ratio as compared to the previously proposed denoising solutions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24088-1_2 | ICIAP (2) |
Keywords | Field | DocType |
edges present,smoothing part,extracting noise element,smoothing criterion,data sample,transition present,noise statistic,noise ratio,heuristic approach,spatial domain,shrinkage approach,mean square error,edge detection,denoising | Noise reduction,Wiener filter,Pattern recognition,Non-local means,Edge detection,Computer science,Signal-to-noise ratio,Smoothing,Lipschitz continuity,Artificial intelligence,Wavelet transform | Conference |
Volume | Issue | ISSN |
6979 | II | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jalil Bushra | 1 | 7 | 4.84 |
| Fauvet Eric | 2 | 7 | 6.12 |
| Olivier Laligant | 3 | 38 | 10.66 |