Abstract
In this paper, the learning-based single image super-resolution (SR) is regarded as a problem of space structure learning. We propose a new SR method that identifies a space from the low-resolution (LR) image space that best preserves the structure of the high-resolution (HR) image space. The inference between the two structure-consistent spaces proves to be accurate and predicts HR image patches with higher quality. An effective iterative algorithm is also proposed to find the near-optimal solution to the model, which can be easily implemented in parallel computing. Extensive experiments are performed to show the effectiveness of the proposed algorithm.
Original language | English (US) |
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Pages (from-to) | 2094-2101 |
Number of pages | 8 |
Journal | Pattern Recognition Letters |
Volume | 34 |
Issue number | 16 |
DOIs | |
State | Published - 2013 |
Keywords
- Metric learning
- Single image super-resolution
- Space structure learning
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence