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Single-image super-resolution via a lightweight convolutional neural network with improved shuffle learning

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Abstract

With the development of deep learning techniques, single-image super-resolution methods based on deep learning have made great progress, enabling significant improvements in image quality and detail reproduction. However, deep convolutional neural networks are often complicated and hard to be understood, and the computational cost limits the application of the models in practical situations. In order to deploy the network on mobile devices with very limited computing power, we build a refined image super-resolution model based on shuffle learning. Based on extensive experimental results on image super-resolution using three widely used datasets, our model not only achieves high scores on the peak signal-to-noise ratio/structural similarity index matrix, but also is simpler and easier to be implemented than other image super-resolution models.

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Data availability

The dataset used in this paper can be downloaded from https://paperswithcode.com/paper/single-image-super-resolution-from.

References

  1. Chen, X., Yang, R., Guo, C.: A lightweight multi-scale residual network for single image super-resolution. SIViP 16, 1793–1801 (2022). https://doi.org/10.1007/s11760-022-02136-z

    Article  Google Scholar 

  2. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281

    Article  Google Scholar 

  3. Dong, C., Loy, C. C., Tang, X.: Accelerating the Super-Resolution Convolutional Neural Network In: European Conference on Computer Vision, pp. 391–407 (2016)

  4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.48550/arXiv.1512.03385.

  5. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646–1654 (2016)

  6. Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1637–1645 (2016)

  7. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

  8. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: IEEE 2010 20th International Conference on Pattern Recognition, pp. 2366–2369(2010)

  9. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818–2826 (2016)

  10. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. (2017).

  11. Zhang, X., Zhou, X., Lin, M., & Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848–6856 (2018).

  12. Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: 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 (CVPR), pp. 1874–1883 (2016)

  13. Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process. 29(6), 1153–1160 (1981). https://doi.org/10.1109/TASSP.1981.1163711

    Article  MathSciNet  Google Scholar 

  14. Boor, C.D.: Bicubic spline interpolation. J. Math. Phys. 41(3), 212–218 (1962)

    Article  MathSciNet  Google Scholar 

  15. Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11), 4544–4556 (2012). https://doi.org/10.1109/TIP.2012.2208977

    Article  MathSciNet  Google Scholar 

  16. Stark, H., Oskoui, P.: High-resolution image recovery from image-plane arrays, using convex projections. J. Opt. Soc. Am. A 6(11), 1715–1726 (1989). https://doi.org/10.1364/josaa.6.001715

    Article  Google Scholar 

  17. Ahn, N., Kang, B., Sohn, K.-A.: Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network, pp. 256–272. Springer, Cham (2018)

    Google Scholar 

  18. Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147–3155 (2017)

  19. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)

  20. Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for superresolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1664–1673 (2018)

  21. He, Z., Ding, B., Fu, G., et al.: Single-image super-resolution via selective multi-scale network. SIViP 16, 937–945 (2022). https://doi.org/10.1007/s11760-021-02038-6

    Article  Google Scholar 

  22. Zhang, Y., Yang, S., Sun, Y., et al.: Attention-guided multi-path cross-CNN for underwater image super-resolution. SIViP 16, 155–163 (2022). https://doi.org/10.1007/s11760-021-01969-4

    Article  Google Scholar 

  23. Tan, M., & Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp. 6105–6114 (2019)

  24. Lu, Z., Chen, Y.: Single image super-resolution based on a modified U-net with mixed gradient loss. SIViP 16, 1143–1151 (2022)

    Article  Google Scholar 

  25. Chen, W., Liu, C., Yan, Y., Jin, L., Sun, X., Peng, X.: Guided dual networks for single image super-resolution. IEEE Access 8, 93608–93620 (2020). https://doi.org/10.1109/ACCESS.2020.2995175

    Article  Google Scholar 

  26. Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8), 2226–2238 (2006)

    Article  Google Scholar 

  27. Yang, X., Zhang, Y., Zhou, D., Yang, R.: An improved iterative back projection algorithm based on ringing artifacts suppression. Neurocomputing 162, 171–179 (2015). https://doi.org/10.1016/j.neucom.2015.03.055

    Article  Google Scholar 

  28. Zhang, Y., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks, pp. 294–310. Springer, Cham (2018)

    Google Scholar 

  29. Huang, J. B, A. Singh, N. Ahuja.: Single image super-resolution from transformed self-exemplars. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197–5206 (2015)

  30. Lai, W. S., Huang, J. B., Ahuja, N., Yang, M. H.: Deep Laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 5835–5843 (2017)

  31. Lim, B., Son, S., Kim, H., Nah, S., Lee, K. M.: Enhanced deep residual networks for single image super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132–1140 (2017)

  32. Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2472–2481(2018). https://doi.org/10.1109/CVPR.2018.00262.

  33. Jian, S., Xu, Z., Shum, H. Y.: Image super-resolution using gradient profile prior. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8(2008). https://doi.org/10.1109/CVPR.2008.4587659.

  34. Zhou, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/tip.2003.819861

    Article  Google Scholar 

  35. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014). arXiv preprint arXiv:1409.1556.

  36. Muhammad, W., Bhutto, Z., Ansari, A., Memon, M.L., Kumar, R., Hussain, A., et al.: Multi-path deep CNN with residual inception network for single image super-resolution. Electronics 10(16), 1979 (2021)

    Article  Google Scholar 

  37. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

  38. Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight Image Super-Resolution with Information Multi-distillation Network. In: Proceedings of the 27th ACM International Conference on Multimedia (2019)

  39. He, K., Zhang, X., Ren, S., & Sun, J.: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), 1026–1034(2015). https://doi.org/10.1109/ICCV.2015.123.

  40. Ma, N., Zhang, X., Zheng, H. T., & Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV), pp. 116–131 (2018).

  41. Yang, X., Zhu, Y., Guo, Y., Zhou, D.: An image super-resolution network based on multi-scale convolution fusion. Vis Comput 38(12), 1–11 (2021). https://doi.org/10.1007/s00371-021-02297-x

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Energy and Electrical Engineering, Hohai University Nanjing, Nanjing, 211100, People’s Republic of China

    Xinbiao Lu, Xupeng Xie, Chunlin Ye, Hao Xing, Zecheng Liu & Yudan Chen

Authors
  1. Xinbiao Lu
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  2. Xupeng Xie
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  3. Chunlin Ye
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  4. Hao Xing
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  5. Zecheng Liu
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  6. Yudan Chen
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Contributions

XL and XX contributed equally to this manuscript. These authors contributed equally to this work and should be considered co-first authors. XL contributed to conceptualization and methodology. XX was involved in conceptualization, data curation, methodology, and writing. CY contributed to conceptualization and provided software. HX was involved in writing—reviewing and editing, and supervision. ZL contributed to resources. YC was involved in resources.

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Correspondence to Xupeng Xie.

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Lu, X., Xie, X., Ye, C. et al. Single-image super-resolution via a lightweight convolutional neural network with improved shuffle learning. SIViP 18, 233–241 (2024). https://doi.org/10.1007/s11760-023-02730-9

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