Name
Abstract | ||
|---|---|---|
Once image motion is accurately estimated, we can utilize those motion estimates for image sharpening and we can remove motion blurs. First, this paper presents a variational motion de-blurring method using a spatially variant model of motion blurs. The standard variational method is not proper for the motion de-blurring, because it is sensitive to model errors, and occurrence of errors are inevitable in motion estimation. To improve the robustness against the model errors, we employ a nonlinear robust estimation function for measuring energy to be minimized. Secondly, we experimentally compare the variational method with our previously presented PDE-based method that does not need any accurate blur model. |
Year | Venue | Keywords |
|---|---|---|
2006 | Florence | image restoration,motion estimation,nonlinear estimation,pde-based method,image motion deblurring,image sharpening,model error,model-based robust variational method,nonlinear robust estimation function,spatially variant model,standard variational method |
Field | DocType | ISSN |
Sharpening,Computer vision,Nonlinear system,Image motion,Variational method,Robustness (computer science),Artificial intelligence,Motion estimation,Mathematics | Conference | 2219-5491 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takahiro Saito | 1 | 100 | 30.46 |
| Taishi Sano | 2 | 0 | 0.34 |
| Takashi Komatsu | 3 | 113 | 33.96 |