Name
Abstract | ||
|---|---|---|
n image denoising algorithm via wiener filtering in the shearlet domain is proposed in this paper, it makes full use of the advantages of them. Shearlets have the features of directionality, localization, anisotropy and multiscale, the image can be decomposed more accurately, and the noise information locates at the high frequency contents in the frequency spectrum, which can help the removal of noise. The wiener filtering is based on minimizing the mean square error criteria; and it has a good performance on removing the Gaussian white noise. So the combination between them can remove noise more effectively. The noisy image is decomposed by the shearlet transform at any scales and in any directions firstly, the high and low frequency coefficients are thus acquired. And then, in the shearlet domain, the high frequency parts are filtered by wiener filtering. Finally, the inverse shearlet transform is adopted to obtain the denoised image. At the end of paper, the experiments show that the proposed algorithm could get better results than others. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11042-012-1290-y | Multimedia Tools and Applications |
Keywords | Field | DocType |
minimizing the mean square error criteria,shearlets,shrinkage factor,image denoising,wiener filtering,shearlet domain | Wiener filter,Inverse,Computer vision,Anisotropy,Pattern recognition,Computer science,Wiener deconvolution,Mean squared error,Shearlet,White noise,Artificial intelligence,Image denoising | Journal |
Volume | Issue | ISSN |
71 | 3 | 1573-7721 |
Citations | PageRank | References |
2 | 0.37 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengfei Xu | 1 | 109 | 27.58 |
| Qiguang Miao | 2 | 355 | 49.69 |
| Xing Tang | 3 | 4 | 1.08 |
| Junying Zhang | 4 | 86 | 7.59 |