  Real-world Super-Resolution (SR) has been traditionally tackled by first
learning a specific degradation model that resembles the noise and corruption
artifacts in low-resolution imagery. Thus, current methods lack generalization
and lose their accuracy when tested on unseen types of corruption. In contrast
to the traditional proposal, we present Robust Super-Resolution (RSR), a method
that leverages the generalization capability of adversarial attacks to tackle
real-world SR. Our novel framework poses a paradigm shift in the development of
real-world SR methods. Instead of learning a dataset-specific degradation, we
employ adversarial attacks to create difficult examples that target the model's
weaknesses. Afterward, we use these adversarial examples during training to
improve our model's capacity to process noisy inputs. We perform extensive
experimentation on synthetic and real-world images and empirically demonstrate
that our RSR method generalizes well across datasets without re-training for
specific noise priors. By using a single robust model, we outperform
state-of-the-art specialized methods on real-world benchmarks.
