Name
Abstract | ||
|---|---|---|
In learning based single image super-resolution(SR) approach, the super-resolved image are usually found or combined from training database through patch matching. But because the representation ability of small patch is limited, it is difficult to guarantee that the super-resolved image is best under global view. To tackle this problem, we propose a statistical learning method for SR with both global and local constraints. Firstly, we use Maximum a Posteriori (MAP) estimation with learned image priors by Fields of Experts (FoE) model, and regularize SR globally guided by the image priors. Secondly, for each overlapped patch, the higher-order Markov random fields (MRFs) is used to model its local relationship with corresponding high-resolution candidates, then belief propagation is used to find high-resolution image. Compared with traditional patch based learning method without global constraint, our method could not only preserve the global image structure, but also restore the local details well. Experiments verify the idea of our global and local constraint SR method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICME.2009.5202565 | ICME |
Keywords | Field | DocType |
high-resolution image,image prior,local constraint,global image structure,super resolution,global constraint,single image super-resolution,local detail,local constraint sr method,global view,super-resolved image,statistical analysis,maximum a posteriori estimation,maximum likelihood estimation,image resolution,high resolution,estimation,learning artificial intelligence,higher order,belief propagation,strontium,data mining,databases,markov processes,map | Computer vision,Random field,Markov process,Pattern recognition,Computer science,Markov chain,Artificial intelligence,Maximum a posteriori estimation,Prior probability,Superresolution,Image resolution,Belief propagation | Conference |
ISSN | Citations | PageRank |
1945-7871 | 1 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Guo | 1 | 15 | 1.94 |
| Xiaokang Yang | 2 | 3581 | 238.09 |
| Rui Zhang | 3 | 92 | 7.65 |
| Yu Song | 4 | 356 | 52.74 |