Localization and saturation of degradation space for weakly-supervised real-world super-resolution

Abstract

Degradation in the real world is highly complex and random, making real super-resolution datasets expensive. A series of unsupervised methods attempt to model real degradations to address the lack of paired training data in real-world super-resolution tasks. However, these unsupervised methods cannot accurately locate the degradation space and the modeled degradation lacks diversity, making it unable to efficiently saturate the degradation space and leading to suboptimal solutions. In this study, we propose a novel degradation space localization and saturation (DSLS) method for weakly-supervised real-world SR, that can accurately localize the real degradation space to generate diverse degradation samples for training. Specifically, the proposed method uses a few paired data to locate the location of the real degradation space and unaligned data to further precisely locate the boundary of the real degradation space, thereby allowing the generated training samples to possess reasonable real degradation. Furthermore, we propose a degradation modulation mechanism to modulate the degradation strength and direction of the generated training samples, so that the generated training samples can efficiently and accurately saturate the real degradation space, making the trained super-resolution model more robust. Experimental results on real datasets demonstrate that our method can be plug-and-play embedded into the training phase of existing methods, while considerably improving the real super-resolution performance with a slight increase in training cost. We also conduct detailed ablation experiments to analyze and validate our contributions.