CAN: Cascade Augmentations against Noise for Image Restoration.

Image restoration aims to recover the latent clean image from a degraded counterpart. In general, the prevailing state-of-the-art image restoration methods concentrate on solving only a specific degradation type according to the task, e.g. deblurring or deraining. However, if the corresponding well-trained frameworks confront other real-world image corruptions, i.e., the corruptions are not covered in the training phase, and state-of-the-art restoration models will suffer from a lack of generalization ability. We have observed that an image restoration model can be easily confused by noise corruption. Towards improving the robustness of image restoration networks, in this paper, we focus on alleviating the corruption of noise in various image restoration tasks, which is almost inevitable in real-world scenes. To this end, we devise a novel Multifarious Augmentation strategy against Noise (CAN) to enhance the robustness of specific image restoration. Specifically, the given degraded images are sequentially augmented from different perspectives, i.e., noise-aware augmentation, and model-aware augmentation. The noise-aware augmentation is proposed to enrich the samples by introducing various noise operations. Moreover, to adapt to more unknown corruptions, we propose a novel model-aware augmentation mechanism, which enhances the scalability by exploring useful both spatial and frequency clues with the help of model randomness. It is worth noting that the proposed augmentation scheme is model-agnostic, and it can plug and play into arbitrary state-of-the-art image restoration architectures. In addition, we construct noise corruption benchmark datasets, derived from the validation set of standard image restoration datasets, to assist us in evaluating the robustness of restoration networks. Extensive quantitative and qualitative evaluations demonstrate that the proposed method has strong generalization capability which can enhance the robustness of various image restoration frameworks when facing diverse noises.