Name
Title | ||
|---|---|---|
ANCHORED NEIGHBORHOOD REGRESSION BASED SINGLE IMAGE SUPER-RESOLUTION FROM SELF-EXAMPLES |
Abstract | ||
|---|---|---|
In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a high resolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based on anchored neighborhood regression to transform LR space into HR space. Moreover, we utilize the flipped and rotated versions of the self-examples to expand the internal patch space. Experimental comparison on standard benchmarks with state-of-the-art methods validates the effectiveness of the proposed approach. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | single image super-resolution,self examples,anchored neighborhood regression |
Field | DocType | ISSN |
Iterative reconstruction,Computer vision,Pattern recognition,Feature detection (computer vision),Computer science,Image texture,Binary image,Image processing,Artificial intelligence,Linear map,Image restoration,Image resolution | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-4673-9961-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Yapeng | 1 | 147 | 9.54 |
| Zhou | 2 | 78 | 11.31 |
| WM | 3 | 221 | 34.28 |
| Shang Xuesen | 4 | 0 | 0.34 |
| QM | 5 | 464 | 72.05 |