Name
Abstract | ||
|---|---|---|
Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor x 8, our method performs favorably against other methods in terms of gradients similarity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3837/tiis.2019.08.007 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Super-resolution,Stage-GAN,Generative adversarial networks,Semantic maps,Large scaling factors | Computer graphics (images),Computer science,Superresolution,Distributed computing,Semantic map | Journal |
Volume | Issue | ISSN |
13 | 8 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhensong Wei | 1 | 0 | 0.68 |
| Bai Huihui | 2 | 243 | 41.01 |
| Yao Zhao | 3 | 1926 | 219.11 |