Name
Abstract | ||
|---|---|---|
Super Resolution (SR) technique aims to reconstruct the high resolution (HR) image from the observed low resolution (LR) one, which is a significant application in our daily life. In this paper, we propose a novel structure named hier-archical recursive network (HRN), which consists of several sub networks and will reconstruct the HR progressively. In each sub network, the LR feature map will be used as input, the contextual information will be explored and the predicted residuals together with the transposed convolutional outputs will be fused to the finer one. Besides, our network can generate multi-scale HR images with a single model and thus is potentially useful in practical applications. Extensive experi-mental results show that our proposed method can achieve the state-of-the-art performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMEW.2019.00108 | 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | Field | DocType |
Single image super resolution, progres sive reconstruction, hierarchical recursive network | Computer vision,Contextual information,Pattern recognition,Computer science,Artificial intelligence,Superresolution,Recursion | Conference |
ISSN | ISBN | Citations |
2330-7927 | 978-1-5386-9215-8 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minglan Su | 1 | 0 | 0.34 |
| Shenqi Lai | 2 | 0 | 1.01 |
| Zhenhua Chai | 3 | 12 | 6.59 |
| Xiaoming Wei | 4 | 56 | 7.33 |
| Yong Liu | 5 | 2 | 2.15 |