Name
Title | ||
|---|---|---|
Gaussian mixture model learning based image denoising method with adaptive regularization parameters. |
Abstract | ||
|---|---|---|
Gaussian mixture model learning based image denoising as a kind of structured sparse representation method has received much attention in recent years. In this paper, for further enhancing the denoised performance, we attempt to incorporate the gradient fidelity term with the Gaussian mixture model learning based image denoising method to preserve more fine structures of images. Moreover, we construct an adaptive regularization parameter selection scheme by combing the image gradient with the local entropy of the image. Experiment results show that our proposed method performs an improvement both in visual effects and peak signal to noise values. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-4214-4 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Image denoising,Gaussian mixture model,Adaptive regularization parameter,Gradient fidelity term | Computer vision,Fidelity,Image gradient,Pattern recognition,Non-local means,Computer science,Signal-to-noise ratio,Sparse approximation,Artificial intelligence,Image denoising,Combing,Mixture model | Journal |
Volume | Issue | ISSN |
76 | 9 | 1380-7501 |
Citations | PageRank | References |
2 | 0.36 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwei Zhang | 1 | 353 | 71.98 |
| Jing Liu | 2 | 135 | 45.52 |
| Tong Li | 3 | 2 | 0.36 |
| Yuhui Zheng | 4 | 43 | 9.40 |
| jin wang | 5 | 243 | 36.79 |