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Exploiting Self-Supervised Constraints in Image Super-Resolution

arXiv 2404.00260 ↗First posted 30 Mar 2024cs.CV eess.IVcited by 0

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Abstract (arXiv)

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.

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References · 15

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  4. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  5. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  6. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  7. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
  8. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  9. 2021Contrastive Learning for Compact Single Image Dehazingcited by 219
  10. 2020Cross-Scale Internal Graph Neural Network for Image Super-Resolutioncited by 94
  11. 2019A Deep Journey into Super-resolution: A surveycited by 86
  12. 2023Curricular Contrastive Regularization for Physics-aware Single Image Dehazingcited by 83
3 more
  1. 2021A Practical Contrastive Learning Framework for Single-Image Super-Resolutioncited by 30
  2. 2023Learning from History: Task-agnostic Model Contrastive Learning for Image Restorationcited by 11
  3. 2023Fully 1×1 Convolutional Network for Lightweight Image Super-Resolutioncited by 6

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