Name
Abstract | ||
|---|---|---|
In this paper, we present a new approach to single image super-resolution (SR). The basic idea is to learn a dictionary which can capture the high-order statistics of high-resolution (HR) images. This is of central importance in image SR application, since the high-order statistics play a significant role in the reconstruction of HR image structure. Kernel principal component analysis (KPCA) is used to learn such a dictionary. To reduce the time complexity of learning and testing for KPCA, a sparse solution is adopted. Meanwhile, kernel ridge regression is employed to relate the input low-resolution (LR) image patches and the HR coding coefficients. Experimental results show that the proposed method can effectively reconstruct image details and outperform state-of-the-art algorithms in both quantitative and visual comparisons. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025427 | ICIP |
Keywords | Field | DocType |
kernel principal analysis (KPCA),pre-image,regression,Super-resolution (SR) | Computer vision,Regression,Pattern recognition,Computer science,Kernel ridge regression,Kernel principal component analysis,Coding (social sciences),Artificial intelligence,Image structure,Time complexity,Superresolution | Conference |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingrong Yuan | 1 | 18 | 2.64 |
| WM | 2 | 221 | 34.28 |
| Zhou | 3 | 78 | 11.31 |
| QM | 4 | 464 | 72.05 |