Name
Abstract | ||
|---|---|---|
Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally or at only static scale only, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm, namely, densely residual laplacian network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TPAMI.2020.3021088 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Super-resolution,laplacian attention,multi-scale attention,densely connected residual blocks,deep convolutional neural network | Journal | 44 |
Issue | ISSN | Citations |
3 | 0162-8828 | 4 |
PageRank | References | Authors |
0.45 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeed Anwar | 1 | 80 | 12.28 |
| Nick Barnes | 2 | 577 | 68.68 |