Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

• URL: http://arxiv.org/abs/2003.07119v3
• Date: Thu, 23 Jul 2020 15:26:52 GMT
• Title: Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
• Authors: Majed El Helou, Ruofan Zhou, Sabine S\"usstrunk
• Abstract summary: We present an analysis, in the frequency domain, of degradation- Kernel overfitting in super-resolution. We propose a 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.
• Score: 20.23203428843382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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