Name
Abstract | ||
|---|---|---|
Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. This is why example-based SR methods study upscaling factors up to 4x (or up to 8x for face hallucination). Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose a novel framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE for up to 32x magnification and exploration of the space of super-resolution. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-69538-5_38 | ACCV (4) |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bühler Marcel Christoph | 1 | 1 | 0.35 |
| Andrés Romero | 2 | 9 | 3.33 |
| Radu Timofte | 3 | 1880 | 118.45 |