Name
Abstract | ||
|---|---|---|
Inpainting is an image processing method to automatically restore the lost information according to the existing image information. Inpainting has great application on restoration of the lost information for photographs, text removal of image, and recovery for the loss coding of image, etc. Image restoration based on partial differential equation (PDE) is an important repair technology. To overcome the shortcomings of the existing PDEs in repair process, such as false edge, incomplete interpolation information, a new PDE for image restoration based on image characteristics is proposed. The new PDE applies different diffusion mode for image pixels with the different characteristics, which can effectively protect the edges, angular points, and other important characteristics of the image during the repair process. The experimental results in both gray images and color images show that our method can obviously improve the image visual effect after inpainting compared with different traditional diffusion models. |
Year | Venue | Field |
|---|---|---|
2015 | ICIG | Computer vision,Pattern recognition,Computer science,Interpolation,Image processing,Coding (social sciences),Inpainting,Artificial intelligence,Pixel,Image restoration,Partial differential equation |
DocType | Citations | PageRank |
Conference | 2 | 0.42 |
References | Authors | |
1 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Zhang | 1 | 48 | 14.46 |
| Ying Chen | 2 | 81 | 47.86 |
| Zhitao Xiao | 3 | 4 | 4.87 |
| Lei Geng | 4 | 15 | 11.01 |
| Jun Wu | 5 | 12 | 7.80 |
| Tiejun Feng | 6 | 2 | 0.42 |
| Ping Liu | 7 | 14 | 7.39 |
| Yufei Tan | 8 | 2 | 0.76 |
| Jinjiang Wang | 9 | 2 | 0.42 |