Name
Abstract | ||
|---|---|---|
•We consturct our ETRN inspired by DenseNet to integrate hierarchical features of LR image.•We introduce dilated convolution to enlarge the recptive field.•We progressively reconstruct SR image into two phases to enhace the image quality. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jvcir.2019.04.002 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Super-resolution,Convolutional neural network,Deep residual learning,Dilated convolution | Receptive field,Computer vision,Residual,Pattern recognition,Convolution,Artificial intelligence,Residual neural network,Superresolution,Mathematics,Visual perception,Computation | Journal |
Volume | ISSN | Citations |
61 | 1047-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Yang | 1 | 40 | 10.74 |
| Wenjing Li | 2 | 145 | 42.73 |
| Ronggui Wang | 3 | 44 | 10.06 |
| Lixia Xue | 4 | 8 | 4.56 |
| Min Hu | 5 | 31 | 12.64 |