Name
Title | ||
|---|---|---|
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution. |
Abstract | ||
|---|---|---|
Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. However, there has been limited advancement in video super-resolution (VSR) due to the complex temporal patterns in videos. In this paper, we investigate how to adapt state-of-the-art methods of image super-resolution for video superresolution. The proposed adapting method is straightforward. The information among successive frames is well exploited, while the overhead on the original image superresolution method is negligible. Furthermore, we propose a learning -based method to ensemble the outputs from multiple super-resolution models. Our methods show superior performance and rank second in the NTIRE2019 Video Super-Resolution Challenge Track I. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPRW.2019.00255 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
DocType | Volume | ISSN |
Conference | abs/1905.02462 | 2160-7508 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Li | 1 | 3 | 1.74 |
| He, D. | 2 | 33 | 13.67 |
| Xiao Liu | 3 | 284 | 41.90 |
| Yukang Ding | 4 | 11 | 1.88 |
| Shilei Wen | 5 | 79 | 13.59 |