Exploiting Self-Supervised Constraints in Image Super-Resolution
Gang Wu Junjun Jiang Kui Jiang Xianming Liu
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 https://github.com/Aitical/SSCSR.