Name
Abstract | ||
|---|---|---|
In this letter, we propose a novel approach for single-image super-resolution (SR). Our method is based on the idea of learning a dictionary which can capture the high-order statistics of high-resolution (HR) images. It 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 adopted to learn such a dictionary. A compact solution is adopted to reduce the time complexity of learning and testing for KPCA. Meanwhile, kernel ridge regression is employed to connect the input low-resolution (LR) image patches with the HR coding coefficients. Experimental results show that the proposed method is effective and efficient in comparison with state-of-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2014.2360038 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Kernel principal analysis (KPCA),Pre-image,Regression,Super-resolution (SR) | Principal component regression,Pattern recognition,Kernel principal component analysis,Coding (social sciences),Artificial intelligence,Time complexity,Image structure,Variable kernel density estimation,Superresolution,Kernel regression,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 3 | 1070-9908 |
Citations | PageRank | References |
11 | 0.53 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Zhou | 1 | 29 | 8.98 |
| Tingrong Yuan | 2 | 18 | 2.64 |
| WM | 3 | 221 | 34.28 |
| QM | 4 | 464 | 72.05 |