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Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12361)

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.

Keywords

Image restoration Super-resolution Denoising Kernel overfitting 

Supplementary material

504471_1_En_44_MOESM1_ESM.pdf (21.1 mb)
Supplementary material 1 (pdf 21594 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer and Communicatison Sciences, EPFLLausanneSwitzerland

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