Name
Title | ||
|---|---|---|
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution |
Abstract | ||
|---|---|---|
The omnidirectional images (ODIs) are usually at low-resolution, due to the constraints of collection, storage and transmission. The traditional two-dimensional (2D) image super-resolution methods are not effective for spherical ODIs, because ODIs tend to have non-uniformly distributed pixel density and varying texture complexity across latitudes. In this work, we propose a novel latitude adaptive upscaling network (LAU-Net) for ODI super-resolution, which allows pixels at different latitudes to adopt distinct upscaling factors. Specifically, we introduce a Laplacian multi-level separation architecture to split an ODI into different latitude bands, and hierarchically upscale them with different factors. In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands. To the best of our knowledge, LAU-Net is the first attempt to consider the latitude difference for ODI super-resolution. Extensive results demonstrate that our LAU-Net significantly advances the super-resolution performance for ODIs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00907 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Deng | 1 | 0 | 1.35 |
| Hao Wang | 2 | 216 | 56.92 |
| Mai Xu | 3 | 509 | 57.90 |
| Yichen Guo | 4 | 0 | 1.01 |
| Yuhang Song | 5 | 0 | 0.34 |
| Li Yang | 6 | 0 | 0.34 |