Robust Image Denoising Through Adversarial Frequency Mixup

Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han

Abstract: Image denoising approaches based on deep neural net-works often struggle with overfitting to specific noise distributions present in training data. This challenge per-sists in existing real-world denoising networks, which are trained using a limited spectrum of real noise distributions, and thus, show poor robustness to out-of-distribution real noise types. To alleviate this issue, we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics, all the while preserving the properties of authentic real-world noise. Subsequently, incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses, con-ducted on a wide range of real noise benchmarks demon-strate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://github.com/dhryougit/AFM.