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Efficient Image Super-Resolution via Symmetric Visual Attention Network (2401.08913v1)

Published 17 Jan 2024 in cs.CV and eess.IV

Abstract: An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms. Recently, efficient Super-Resolution (SR) research focuses on reducing model complexity and improving efficiency through improved deep small kernel convolution, leading to a small receptive field. The large receptive field obtained by large kernel convolution can significantly improve image quality, but the computational cost is too high. To improve the reconstruction details of efficient super-resolution reconstruction, we propose a Symmetric Visual Attention Network (SVAN) by applying large receptive fields. The SVAN decomposes a large kernel convolution into three different combinations of convolution operations and combines them with an attention mechanism to form a Symmetric Large Kernel Attention Block (SLKAB), which forms a symmetric attention block with a bottleneck structure by the size of the receptive field in the convolution combination to extract depth features effectively as the basic component of the SVAN. Our network gets a large receptive field while minimizing the number of parameters and improving the perceptual ability of the model. The experimental results show that the proposed SVAN can obtain high-quality super-resolution reconstruction results using only about 30% of the parameters of existing SOTA methods.

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References (42)
  1. Ntire 2017 challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126–135, 2017.
  2. Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the European conference on computer vision (ECCV), pages 252–268, 2018.
  3. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In Proceedings of the British Machine Vision Conference, 2012.
  4. Activating more pixels in image super-resolution transformer. arXiv preprint arXiv:2205.04437, 2022.
  5. Fast, accurate and lightweight super-resolution with neural architecture search. In 2020 25th International conference on pattern recognition (ICPR), pages 59–64. IEEE, 2021.
  6. Is image super-resolution helpful for other vision tasks? In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1–9. IEEE, 2016.
  7. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11963–11975, 2022.
  8. Accelerating the super-resolution convolutional neural network. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14, pages 391–407. Springer, 2016.
  9. Remote sensing image super-resolution using second-order multi-scale networks. IEEE Transactions on Geoscience and Remote Sensing, 59(4):3473–3485, 2020.
  10. Lightweight bimodal network for single-image super-resolution via symmetric cnn and recursive transformer. arXiv preprint arXiv:2204.13286, 2022.
  11. Image super-resolution using knowledge distillation. In Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part II, pages 527–541. Springer, 2019.
  12. Multi-scale high-resolution vision transformer for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12094–12103, 2022.
  13. Visual attention network. arXiv preprint arXiv:2202.09741, 2022.
  14. Hierarchical neural architecture search for single image super-resolution. IEEE Signal Processing Letters, 27:1255–1259, 2020.
  15. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016.
  16. Single image super-resolution from transformed self-exemplars. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5197–5206, 2015.
  17. Fast and accurate single image super-resolution via information distillation network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 723–731, 2018.
  18. Lightweight image super-resolution with information multi-distillation network. In Proceedings of the 27th acm international conference on multimedia, pages 2024–2032, 2019.
  19. Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1637–1645, 2016.
  20. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  21. Residual local feature network for efficient super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 766–776, 2022.
  22. A review of the deep learning methods for medical images super resolution problems. Irbm, 42(2):120–133, 2021.
  23. Ntire 2022 challenge on efficient super-resolution: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1062–1102, 2022.
  24. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1833–1844, 2021.
  25. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 136–144, 2017.
  26. Residual feature distillation network for lightweight image super-resolution. In Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pages 41–55. Springer, 2020.
  27. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021.
  28. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976–11986, 2022.
  29. Deep constrained least squares for blind image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17642–17652, 2022.
  30. Fast nearest convolution for real-time efficient image super-resolution. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II, pages 561–572. Springer, 2023.
  31. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, volume 2, pages 416–423. IEEE, 2001.
  32. Single image super-resolution via a holistic attention network. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16, pages 191–207. Springer, 2020.
  33. Single image resolution enhancement by efficient dilated densely connected residual network. Signal Processing: Image Communication, 79:13–23, 2019.
  34. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1874–1883, 2016.
  35. Learning to zoom-in via learning to zoom-out: Real-world super-resolution by generating and adapting degradation. IEEE Transactions on Image Processing, 30:2947–2962, 2021.
  36. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  37. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
  38. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728–5739, 2022.
  39. On single image scale-up using sparse-representations. In Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7, pages 711–730. Springer, 2012.
  40. Edge-oriented convolution block for real-time super resolution on mobile devices. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4034–4043, 2021.
  41. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV), pages 286–301, 2018.
  42. Rethinking bottleneck structure for efficient mobile network design. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pages 680–697. Springer, 2020.
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Authors (6)
  1. Chengxu Wu (1 paper)
  2. Qinrui Fan (3 papers)
  3. Shu Hu (63 papers)
  4. Xi Wu (100 papers)
  5. Xin Wang (1306 papers)
  6. Jing Hu (50 papers)
Citations (1)
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