Name
Abstract | ||
|---|---|---|
In this paper, a shock-diffusion model is presented to restore both blurred and noisy image. The proposed approach uses a half smoothing kernel to get the precise edge directions, and use different shock-diffusion strategies for different image regions. Experiment results on real images show that the proposed model can effectively eliminate noise and enhance edges while preserving small objects and corners simultaneously. Compared to other approaches, the proposed method offers both better visual results and qualitative measurements. |
Year | DOI | Venue |
|---|---|---|
2013 | null | VISAPP (1) |
Keywords | Field | DocType |
Deblurring,Half Gaussian kernel,Image regularization,Shock filter | Computer vision,Deblurring,Pattern recognition,Computer science,Gaussian,Regularization (mathematics),Artificial intelligence | Conference |
Volume | Issue | Citations |
1 | null | 1 |
PageRank | References | Authors |
0.35 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baptiste Magnier | 1 | 69 | 12.21 |
| Huanyu Xu | 2 | 1 | 0.69 |
| Philippe Montesinos | 3 | 185 | 24.07 |