Name
Title | ||
|---|---|---|
Photo-realistic Image Super-resolution with Fast and Lightweight Cascading Residual Network. |
Abstract | ||
|---|---|---|
Recent progress in the deep learning-based models has improved single-image super-resolution significantly. However, despite their powerful performance, many models are difficult to apply to the real-world applications because of the heavy computational requirements. To facilitate the use of a deep learning model in such demands, we focus on keeping the model fast and lightweight while maintaining its accuracy. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. Moreover, we adopt group convolution and weight-tying for our proposed model in order to achieve extreme efficiency. In addition to the traditional super-resolution task, we apply our methods to the photo-realistic super-resolution field using the adversarial learning paradigm and a multi-scale discriminator approach. By doing so, we show that the performances of the proposed models surpass those of the recent methods, which have a complexity similar to ours, for both traditional pixel-based and perception-based tasks. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1903.02240 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Namhyuk Ahn | 1 | 6 | 3.13 |
| Kang, Byungkon | 2 | 21 | 3.77 |
| Kyung-Ah Sohn | 3 | 38 | 13.32 |