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

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks