Advertisement

Signal, Image and Video Processing

, Volume 11, Issue 5, pp 961–968 | Cite as

A fast single-image super-resolution via directional edge-guided regularized extreme learning regression

  • Paheding SidikeEmail author
  • Evan Krieger
  • M. Zahangir Alom
  • Vijayan K. Asari
  • Tarek Taha
Original Paper

Abstract

The goal of super-resolution (SR) is to increase the spatial resolution of a low-resolution (LR) image by a certain factor using either single or multiple LR input images. This paper presents a machine learning-based approach to reconstruct a high-resolution (HR) image from a single LR image. Inspired by the human visual cortex system, which is sensitive to high-frequency (HF) components in an image, we aim to model this concept by training a neural network to estimate the missing HF components that contain structural details. In our method, various directional edge responses at each pixel are considered to obtain more complete HF information and then a regularized extreme learning regression model is trained using a set of LR and HR images. Finally, the trained system is applied to a LR image to generate HR image. The experimental results confirm the effectiveness and efficiency of the proposed scheme in comparison with the state-of-the-art SR methods.

Keywords

Super-resolution Directional edges Extreme learning regression Structural similarity 

Supplementary material

11760_2016_1045_MOESM1_ESM.pdf (520 kb)
Supplementary material 1 (pdf 520 KB)

References

  1. 1.
    Prendergast, R.S., Nguyen, T.Q.: A block-based super-resolution for video sequences. In: IEEE International Conference on Image Processing, pp. 1240–1243 (2008)Google Scholar
  2. 2.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  3. 3.
    Carrato, S., Ramponi, G., Marsi, S.: A simple edge-sensitive image interpolation filter. Int. Conf. Image Process. 3, 711–7143 (1996)Google Scholar
  4. 4.
    Allebach, J., Wong, P.W.: Edge-directed interpolation. In: Image Processing, Proceedings, International Conference on, vol. 3, pp. 707–710 (1996)Google Scholar
  5. 5.
    Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Sun, J., Sun, J., Xu, Z., Shum, H.-Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  8. 8.
    Sun, J., Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529–1542 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wang, L., Xiang, S., Meng, G., Wu, H., Pan, C.: Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans. Circuits Syst. Video Technol. 23(8), 1289–1299 (2013)CrossRefGoogle Scholar
  10. 10.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  11. 11.
    Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    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)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)Google Scholar
  14. 14.
    Gao, X., Zhang, K., Tao, D., Li, X.: Image super-resolution with sparse neighbor embedding. IEEE Trans. Image Process. 21(7), 3194–3205 (2012)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Chen, C., Fowler, J.E.: Single-image super-resolution using multihypothesis prediction. In: 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 608–612 (2012)Google Scholar
  16. 16.
    Timofte, R., Smet, V., Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision, pp. 1920–1927 (2013)Google Scholar
  17. 17.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)CrossRefGoogle Scholar
  18. 18.
    Huang, G.-B., Chen, L., Siew, C.-K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879–892 (2006)CrossRefGoogle Scholar
  19. 19.
    Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(2), 513529 (2012)Google Scholar
  20. 20.
    An, L., Bhanu, B.: Image super-resolution by extreme learning machine. In: IEEE International Conference on Image Processing (ICIP), pp. 2209–2212 (2012)Google Scholar
  21. 21.
    Robinson, Gner S.: Edge detection by compass gradient masks. Comput. Gr. Image Process. 6(5), 492–501 (1977)CrossRefGoogle Scholar
  22. 22.
    Serre, D.: Matrices: Theory and Applications. Springer, New York (2002)Google Scholar
  23. 23.
    Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and its Applications. Wiley, New York (1971)Google Scholar
  24. 24.
    Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)CrossRefzbMATHGoogle Scholar
  25. 25.
    Wang, Z., 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)CrossRefGoogle Scholar
  26. 26.
    Giachetti, A., Asuni, N.: Real-time artifact-free image upscaling. IEEE Trans. Image Process. 20(10), 2760–2768 (2011)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Curves and Surfaces, pp. 711–730 (2010)Google Scholar
  28. 28.
    Yeganli, F., Nazzal, M., Unal, M., Ozkaramanli, H.: Image super-resolution via sparse representation over multiple learned dictionaries based on edge sharpness. Signal Image Video Process. 10(3), 535–542 (2016)CrossRefGoogle Scholar
  29. 29.
    Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: Computer Vision and Pattern Recognition (2008)Google Scholar
  30. 30.
    Mallat, S., Yu, G.: Super-resolution with sparse mixing estimators. IEEE Trans. Image Process. 19(11), 2889–2900 (2010)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Kim, K. I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127–1133 (2010)Google Scholar
  32. 32.
    Tian, J., Ma, K.-K.: A survey on super-resolution imaging. Signal Image Video Process. 5(3), 329–342 (2011)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  34. 34.
    Gao, X., Zhang, K., Tao, D., Li, X.: Joint learning for single-image super-resolution via a coupled constraint. IEEE Trans. Image Process. 21(2), 469–480 (2012)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zhang, K., Tao, D., Gao, X., Li, X., Xiong, Z.: Learning multiple linear mappings for efficient single image super-resolution. IEEE Trans. Image Process. 24(3), 846–861 (2015)Google Scholar
  36. 36.
    Abedi, A., Kabir, E.: Text-image super-resolution through anchored neighborhood regression with multiple class-specific dictionaries. Signal Image Video Process. (2016). doi: 10.1007/s11760-016-0933-2
  37. 37.
    Cui, Z., Chang, H., Shan, S., Zhong, B., Chen, X.: Deep network cascade for image super-resolution. In: European Conference on Computer Vision, pp. 49-64. Springer (2014)Google Scholar
  38. 38.
    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)CrossRefGoogle Scholar
  39. 39.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  40. 40.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Hu, X., Peng, S., Hwang, W.-L.: Learning adaptive interpolation kernels for fast single-image super resolution. Signal Image Video Process. 8(6), 1077–1086 (2014)CrossRefGoogle Scholar
  42. 42.
    Haris, M., Widyanto, M.R., Nobuhara, H.: First-order derivative-based super-resolution. Signal Image Video Process. (2016). doi: 10.1007/ s11760-016-0880-y
  43. 43.
    Zhou, D., Shen, X., Dong, W.: Image zooming using directional cubic convolution interpolation. IET Image Process. 6(6), 627–634 (2012)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569–2582 (2014)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: 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, vol. 2, pp. 416-423. IEEE (2001)Google Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Paheding Sidike
    • 1
    Email author
  • Evan Krieger
    • 1
  • M. Zahangir Alom
    • 1
  • Vijayan K. Asari
    • 1
  • Tarek Taha
    • 1
  1. 1.Department of Electrical and Computer EngineeringUnierstiy of DaytonDaytonUSA

Personalised recommendations