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Single Image Super-Resolution Based on Global-Local Information Synergy

arXiv 2405.01085 ↗First posted 2 May 2024cs.CVcited by 0

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Abstract (arXiv)

Although several image super-resolution solutions exist, they still face many challenges. CNN-based algorithms, despite the reduction in computational complexity, still need to improve their accuracy. While Transformer-based algorithms have higher accuracy, their ultra-high computational complexity makes them difficult to be accepted in practical applications. To overcome the existing challenges, a novel super-resolution reconstruction algorithm is proposed in this paper. The algorithm achieves a significant increase in accuracy through a unique design while maintaining a low complexity. The core of the algorithm lies in its cleverly designed Global-Local Information Extraction Module and Basic Block Module. By combining global and local information, the Global-Local Information Extraction Module aims to understand the image content more comprehensively so as to recover the global structure and local details in the image more accurately, which provides rich information support for the subsequent reconstruction process. Experimental results show that the comprehensive performance of the algorithm proposed in this paper is optimal, providing an efficient and practical new solution in the field of super-resolution reconstruction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 57 references that are in the map. First posted on arXiv 2 May 2024.

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32 of 57 references.

References · 32

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
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20 more
  1. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 394
  2. 2015Sketch-based Manga Retrieval using Manga109 Datasetcited by 329
  3. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  4. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  5. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 264
  6. 2017MemNet: A Persistent Memory Network for Image Restorationcited by 244
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  8. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
  9. 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 178
  10. 2016Deep Pyramidal Residual Networkscited by 163
  11. 2019Feedback Network for Image Super-Resolutioncited by 155
  12. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  13. 2019Recurrent Back-Projection Network for Video Super-Resolutioncited by 148
  14. 2017Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networkscited by 138
  15. 2020Structure-Preserving Super Resolution with Gradient Guidancecited by 116
  16. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
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  18. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 57
  19. 2022Residual Local Feature Network for Efficient Super-Resolutioncited by 53
  20. 2023SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolutioncited by 18

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