Name
Abstract | ||
|---|---|---|
Image super-resolution is an important research field in image analysis. The techniques of image super-resolution has been widely used in many computer vision applications. In recent years, the success of deep learning methods in image super-resolution have attracted more and more researchers. This paper gives a brief review of recent deep learning based methods for single image super-resolution (SISR), in terms of network type, network structure, and training methods. The advantages and disadvantages of these methods are analyzed as well. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Superresolution,Machine learning,Network structure |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
30 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yungang Zhang | 1 | 87 | 10.05 |
| Yu Xiang | 2 | 82 | 18.60 |