  Super-resolution (SR) is by definition ill-posed. There are infinitely many
plausible high-resolution variants for a given low-resolution natural image.
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 an explorative facial super-resolution 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 on faces, for up to 32x
magnification and exploration of the space of super-resolution. Our code and
models are available at: https://mcbuehler.github.io/DeepSEE/
