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Fast Image Super-Resolution Based on Limit Gradient Embedding Cascaded Forest

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Abstract

At present, the deep learning super-resolution (SR) method has achieved excellent results, but it also faces problems such as large models, high computational cost, a large amounts of training data, and poor interpretability. However, traditional machine learning-based methods still have room for improvement in feature extraction and model structure. This paper constructs a gradient embedding cascade forest structure on the basis of random forest and proposes a limit gradient embedding cascaded forest SR (LGECFSR) model. In feature construction, we not only adopt the first-order gradient, the second-order gradient, and other features of the image but also fuse the information of the original LR image. In addition, image blocks of different sizes are used for training, which increases the model’s generalization ability. Compared with the state-of-the-art machine learning-based methods, our method achieves the best performance and the second-best computational speed. In addition, compared with some deep learning-based methods, our model has a similar reconstruction effect and the best computational speed. In detail, for some reconstruction tasks, the Multi-Adds of LGECFSR is one-tenth to one-4000th of that of some current models. However, the SR performance of LGECFSR is the same or slightly better than that of some current classical algorithms.

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References

  1. N. Ahn, B. Kang, K.A. Sohn, Fast, accurate, and lightweight super-resolution with cascading residual network, in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 252–268

  2. P. Arbelaez, M. Maire, C. Fowlkes et al., Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2010)

    Article  Google Scholar 

  3. M. Bevilacqua, A. Roumy, C. Guillemot et al., Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012), p. 135-1

  4. A.K. Bhunia, A.K. Bhunia, A. Sain et al., Improving document binarization via adversarial noise-texture augmentation, in 2019 IEEE International Conference on Image Processing (ICIP) (IEEE, 2019), pp. 2721–2725

  5. T. Chen, C. Guestrin, Xgboost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 785–794

  6. X. Chu, B. Zhang, H. Ma et al., Fast, accurate and lightweight super-resolution with neural architecture search. arXiv preprint, arXiv:1901.07261 (2019)

  7. C. Dong, C.C. Loy, K. He et al., Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  8. C. Dong, C.C. Loy, K. He et al., Learning a deep convolutional network for image super-resolution, in European Conference on Computer Vision (Springer, Cham, 2014), pp. 184–199

  9. C. Dong, C.C. Loy, X. Tang, Accelerating the super-resolution convolutional neural network, in European Conference on Computer Vision (Springer, Cham, 2016), pp. 391–407

  10. J. Gu, H. Lu, W. Zuo et al., Blind super-resolution with iterative kernel correction, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019), pp. 1604–1613

  11. P. Gu, C. Jiang, M. Ji et al., Low-dose computed tomography image super-resolution reconstruction via random forests. Sensors 19(1), 207 (2019)

    Article  Google Scholar 

  12. Y. Guo, J. Chen, J. Wang et al., Closed-loop matters: dual regression networks for single image super-resolution, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 5407–5416

  13. S. Gupta, P.P. Roy, D.P. Dogra et al., Retrieval of colour and texture images using local directional peak valley binary pattern. Pattern Anal. Appl. 23(4), 1569–1585 (2020)

    Article  Google Scholar 

  14. J.B. Huang, A. Singh, N. Ahuja, Single image super-resolution from transformed self-exemplars, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 5197–5206

  15. J. Kim, J. Kwon Lee, K. Mu Lee, Accurate image super-resolution using very deep convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and pattern Recognition (2016), pp. 1646–1654

  16. J. Kim, J. Kwon Lee, K. Mu Lee, Deeply-recursive convolutional network for image super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 1637–1645

  17. S. Kim, D. Jun, B.G. Kim et al., Single image super-resolution method using CNN-based lightweight neural networks. Appl. Sci. 11(3), 1092 (2021)

    Article  Google Scholar 

  18. W.S. Lai, J.B. Huang, N. Ahuja et al., Deep Laplacian pyramid networks for fast and accurate super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 624–632

  19. R. Lan, L. Sun, Z. Liu et al., Madnet: a fast and lightweight network for single-image super resolution. IEEE Trans. Cybern. 51(3), 1443–1453 (2020)

    Article  Google Scholar 

  20. C. Ledig, L. Theis, F. Huszár 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 (2017), pp. 4681–4690

  21. Y. Lee, D. Jun, B.G. Kim et al., Enhanced single image super resolution method using lightweight multi-scale channel dense network. Sensors 21(10), 3351 (2021)

    Article  Google Scholar 

  22. H. Li, K.M. Lam, M. Wang, Image super-resolution via feature-augmented random forest. Signal Process.: Image Commun. 72, 25–34 (2019)

    Google Scholar 

  23. B. Lim, S. Son, H. Kim et al., Enhanced deep residual networks for single image super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017), pp. 136–144

  24. N. Liu, X. Xu, Y. Li et al., Sparse representation based image super-resolution on the KNN based dictionaries. Opt. Laser Technol. 110, 135–144 (2019)

    Article  Google Scholar 

  25. Y. Matsui, K. Ito, Y. Aramaki et al., Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appl. 76(20), 21811–21838 (2017)

    Article  Google Scholar 

  26. A. Mittal, P.P. Roy, P. Singh et al., Rotation and script independent text detection from video frames using sub pixel mapping. J. Vis. Commun. Image Represent. 46, 187–198 (2017)

    Article  Google Scholar 

  27. P.P. Roy, A.K. Bhunia, U. Pal, Date-field retrieval in scene image and video frames using text enhancement and shape coding. Neurocomputing 274, 37–49 (2018)

    Article  Google Scholar 

  28. J. Salvador, E. Perez-Pellitero, Naive Bayes super-resolution forest, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 325–333

  29. S. Schulter, C. Leistner, H. Bischof, Fast and accurate image upscaling with super-resolution forests, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3791–3799

  30. Y. Tai, J. Yang, X. Liu et al., Memnet: a persistent memory network for image restoration, in Proceedings of the IEEE International Conference on Computer Vision (2017), pp. 4539–4547

  31. R. Timofte, V. De Smet, L. Van Gool, A+: adjusted anchored neighborhood regression for fast super-resolution, in Asian Conference on Computer Vision (Springer, Cham, 2014), pp. 111–126

  32. S. Wang, L. Zhang, Y. Liang et al., Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2012), pp. 2216–2223

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

  34. J. Yang, J. Wright, T.S. Huang et al., Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  35. W. Yang, W. Wang, X. Zhang et al., Lightweight feature fusion network for single image super-resolution. IEEE Signal Process. Lett. 26(4), 538–542 (2019)

    Article  Google Scholar 

  36. X. Yang, Z. Li, Y. Guo et al., Retinal vessel segmentation based on an improved deep forest. Int. J. Imaging Syst. Technol. (2021). https://doi.org/10.1002/ima.22610

    Article  Google Scholar 

  37. X. Yang, L. Liu, C. Zhu et al., An improved anchor neighborhood regression SR method based on low-rank constraint. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-02022-0

    Article  Google Scholar 

  38. X. Yang, T. Xie, L. Liu et al., Image super-resolution reconstruction based on improved Dirac residual network. Multidim. Syst. Signal Process. 1, 18 (2021). https://doi.org/10.1007/s11045-021-00773-0

    Article  MATH  Google Scholar 

  39. X. Yang, Y. Zhang, Y. Guo et al., An image super-resolution deep learning network based on multi-level feature extraction module. Multimedia Tools Appl. 80(5), 7063–7075 (2021)

    Article  Google Scholar 

  40. R. Zeyde, M. Elad et al., On single image scale-up using sparse representations, in International Conference on Curves and Surfaces (Springer, Berlin, Heidelberg, 2010), pp. 711–730

  41. R. Zeyde, M. Elad, M. Protter, On single image scale-up using sparse-representations, in International Conference on Curves and Surfaces (Springer, Berlin, Heidelberg, 2010), pp. 711–730

  42. C. Zhang, W. Liu, J. Liu et al., Sparse representation and adaptive mixed samples regression for single image super-resolution. Signal Process.: Image Commun. 67, 79–89 (2018)

    Google Scholar 

  43. K. Zhang, W. Zuo, L. Zhang, Learning a single convolutional super-resolution network for multiple degradations, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 3262–3271

  44. M. Zhang, C. Desrosiers, High-quality image restoration using low-rank patch regularization and global structure sparsity. IEEE Trans. Image Process. 28(2), 868–879 (2018)

    Article  MathSciNet  Google Scholar 

  45. X. Zhang, X. Wu, Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation. IEEE Trans. Image Process. 17(6), 887–896 (2008)

    Article  MathSciNet  Google Scholar 

  46. J. Zhao, H. Hu, F. Cao, Image super-resolution via adaptive sparse representation. Knowl.-Based Syst. 124, 23–33 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (61573182, 62073164), and by the Fundamental Research Funds for the Central Universities (NS2020025).

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Authors and Affiliations

  1. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People’s Republic of China

    Xin Yang, Chenhuan Wu, Dake Zhou & Tao Li

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  1. Xin Yang
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Correspondence to Xin Yang.

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Yang, X., Wu, C., Zhou, D. et al. Fast Image Super-Resolution Based on Limit Gradient Embedding Cascaded Forest. Circuits Syst Signal Process 41, 2007–2026 (2022). https://doi.org/10.1007/s00034-021-01869-5

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