Exploiting Self-Supervised Constraints in Image Super-Resolution
This work provides an incremental improvement for researchers and practitioners in image processing by offering a plug-and-play framework to boost super-resolution performance.
The paper tackles the problem of single image super-resolution by introducing a novel self-supervised constraint called SSC-SR, which enhances existing models by addressing divergence in image complexity, resulting in an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR on benchmark datasets.
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.