Abstract
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR. SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a target model updated via exponential moving average to enhance stability. The proposed SSC-SR framework works as a plug-and-play paradigm and can be easily applied to existing SR models. Empirical evaluations reveal that our SSC-SR framework delivers substantial enhancements on a variety of benchmark datasets, achieving an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR. In addition, extensive ablation studies corroborate the effectiveness of each constituent in our SSC-SR framework. Codes are available at this https URL.
Abstract (translated)
近年来在自监督学习方面的进步，主要研究在高级视觉任务上，已经在低级图像处理领域进行了探讨。本文介绍了一种名为SSC-SR的新单图像超分辨率自监督约束，通过采用双非对称范式和通过指数平滑平均更新目标模型来增强稳定性。所提出的SSC-SR框架是一个可插拔和可用的范式，可以轻松应用于现有的SR模型中。实证评估表明，我们的SSC-SR框架在各种基准数据集上取得了显著的增强，实现了EDSR和SwinIR的均值增加0.1 dB。此外，广泛的消融研究证实了SSC-SR框架中每个组成部分的有效性。代码可在此处下载：https://www.example.com/
URL
https://arxiv.org/abs/2404.00260
