  Super-resolution and denoising are ill-posed yet fundamental image
restoration tasks. In blind settings, the degradation kernel or the noise level
are unknown. This makes restoration even more challenging, notably for
learning-based methods, as they tend to overfit to the degradation seen during
training. We present an analysis, in the frequency domain, of
degradation-kernel overfitting in super-resolution and introduce a conditional
learning perspective that extends to both super-resolution and denoising.
Building on our formulation, we propose a stochastic frequency masking of
images used in training to regularize the networks and address the overfitting
problem. Our technique improves state-of-the-art methods on blind
super-resolution with different synthetic kernels, real super-resolution, blind
Gaussian denoising, and real-image denoising.
