Name
Abstract | ||
|---|---|---|
We present a new image denoising method based on adaptive subband decomposition (or adaptive wavelet transform) in which the filter coefficients are updated according to a least mean square (LMS) type algorithm. Adaptive subband decomposition filter banks have the perfect reconstruction property. Since the adaptive filter bank adjusts itself to the changing input environment, denoising is more effective compared to fixed filter banks. Simulation examples are presented |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICIP.2001.959003 | Image Processing, 2001. Proceedings. 2001 International Conference |
Keywords | Field | DocType |
adaptive filters,channel bank filters,image reconstruction,interference suppression,least mean squares methods,wavelet transforms,adaptive filter banks,adaptive subband decomposition,adaptive wavelet transform,image denoising,least mean square algorithm,perfect reconstruction | Least mean squares filter,Computer vision,Pattern recognition,Non-local means,Computer science,Filter bank,Composite image filter,Artificial intelligence,Kernel adaptive filter,Adaptive filter,Recursive least squares filter,Filter design | Conference |
Volume | ISSN | ISBN |
1 | 1522-4880 | 0-7803-6725-1 |
Citations | PageRank | References |
2 | 0.40 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sinan Gezici | 1 | 682 | 66.18 |
| Ismail Yilmaz | 2 | 2 | 0.40 |
| Ömer Nezih Gerek | 3 | 118 | 19.51 |
| A. Enis Çetin | 4 | 871 | 118.56 |