Name
Abstract | ||
|---|---|---|
In this article, a new method of de-noising is proposed, based on the wavelet maxima. The originality of this method is in the use of the gradient angle in a multi-scale framework as the discriminatory parameter. In order to use to the best advantage the angle information, the multi-scale gradient decomposition schema proposed by Mallat is modified thus enabling a computation of uncorrelated partial derivatives. From this computation, a selection method of multi-scale contours is put forward, having a lesser algorithmic complexity than processings based on the gradient norm. The performance of this new algorithm is illustrated using simulated data and angiography images. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1016/S0262-8856(00)00048-2 | Image and Vision Computing |
Keywords | Field | DocType |
Image de-noising,Multi-scale gradient,Wavelet maxima transform,Angular dispersion,Lipschitz's regularity | Harmonic wavelet transform,Pattern recognition,Second-generation wavelet transform,Artificial intelligence,Discrete wavelet transform,Cascade algorithm,Stationary wavelet transform,Wavelet packet decomposition,Mathematics,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
18 | 13 | 0262-8856 |
Citations | PageRank | References |
1 | 0.40 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Carré | 1 | 1 | 0.40 |
| C. Fernandez-Maloigne | 2 | 36 | 4.62 |