Advertisement

A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit

  • Zhuang Zhang
  • Xu Chen
  • Lei LiuEmail author
  • Yefei Li
  • Yubin Deng
Original Paper
  • 8 Downloads

Abstract

The orthogonal matching pursuit algorithm directly samples the image signal by using the sparsity of the image signal. It uses the atom that matches the image signal feature to describe the image, which can better preserve the detailed features of the image. In this paper, an improvement of variable step size and optimized cut-off conditions is made. The experimental results show that the improved algorithm makes the denoised image clearer and have more detailed features.

Keywords

Image denoising Sparse representation Matching pursuit 

Notes

Acknowledgements

This work is supported by Qing Lan Project of Jiangsu Province-China (Grant No. 2017-AD41779), the Fundamental Research Funds for the Central Universities-China (Grant No. 30916011206) and the Six Talent Peaks Project in Jiangsu Province-China (Grant No. 2015-XCL-008).

References

  1. 1.
    Huang, Z., Qian, L., Hao, F., et al.: Iterative weighted nuclear norm for X-ray cardiovascular angiogram image denoising. SIViP 11(8), 1445–1452 (2017)CrossRefGoogle Scholar
  2. 2.
    Tarmissi, K., Hamza, A.B.: Multivariate kernel diffusion for surface denoising. SIViP 5(2), 191–201 (2011)CrossRefGoogle Scholar
  3. 3.
    Lucata, L., Siohanb, P., Barbac, D.: Adaptive and global optimization methods for weighted vector median filters. Signal Process. Image Commun. 17(2), 509–523 (2002)CrossRefGoogle Scholar
  4. 4.
    Luisier, F., Blu, T., Unser, M.: A new sure approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans. Image Process. 16(3), 593–606 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, S., Xia, Y., Liu, Q., et al.: Gabor feature based nonlocal means filter for textured image denoising. J. Vis. Commun. Image Represent. 23(7), 1008–1018 (2012)CrossRefGoogle Scholar
  6. 6.
    Xu, J., Zhang, L., Zuo, W., et al.: Patch group based nonlocal self-similarity prior learning for image denoising. In: Proceedings of IEEE International Conference on Computer Vision. IEEE Computer Society, pp. 244–252 (2015)Google Scholar
  7. 7.
    Osher, S., Burger, M., Goldfarb, D., et al.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Afonso, M.V., Sanches, J.M.R.: A total variation recursive space-variant filter for image denoising. Digital Signal Process. 40, 101–116 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, J., Wang, Y., Su, K., et al.: Image denoising with multidirectional shrinkage in directionlet domain. Signal Process. 125, 64–78 (2016)CrossRefGoogle Scholar
  10. 10.
    Dong, W., Zhang, L., Shi, G., et al.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zhong, H., Ma, K., Zhou, Y.: Modified BM3D algorithm for image denoising using nonlocal centralization prior. Signal Process. 106, 342–347 (2015)CrossRefGoogle Scholar
  12. 12.
    Dong, W., Li, X., Zhang, D., et al.: Sparsity-based image denoising via dictionary learning and structural clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 457–464 (2011)Google Scholar
  13. 13.
    Deng, C.Z.: Image Sparse Representation Theory and Its Application. M. S. thesis, Huazhong University of Science and Technology (2008)Google Scholar
  14. 14.
    Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)CrossRefGoogle Scholar
  15. 15.
    Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Jeon, B., Seokbyeung, O., Oh, S.J.: Fast matching pursuit method with distance comparison. In: Proceedings of IEEE International Conference on Image Processing, pp. 980–983 (2000)Google Scholar
  17. 17.
    Huggins, P.S., Zucker, S.W.: Greedy Basis Pursuit. IEEE Trans. Signal Process. 55(7), 3760–3772 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Liu, Z., Zhang, H.N., Zhang, Y.L., et al.: Image reconstruction algorithm based on weak selection regularization orthogonal matching tracking. Acta Photonica Sin. 41(10), 1217–1221 (2012)CrossRefGoogle Scholar
  19. 19.
    Wu, D., Wang, K.M., Zhao, Y.X., et al.: Stagewise regularized orthogonal matching pursuit algorithm. Opt. Precis. Eng. 22(5), 1395–1402 (2014)CrossRefGoogle Scholar
  20. 20.
    Deng, X.Y., Liu, Z.L.: Image denoising based on steepest descent OMP and K-SVD. In: Proceedings of IEEE International Conference on Signal Processing, Communications and Computing (2015)Google Scholar
  21. 21.
    Bouboulis, P., Papageorgiou, G., Theodoridis, S.: Robust image denoising in RKHS via orthogonal matching pursuit. In: Proceedings of IEEE International Workshop on Cognitive Information Processing (2014)Google Scholar
  22. 22.
    Haghighat, M., Razian, M.A.: Fast-FMI: Non-reference image fusion metric. In: Proceedings of IEEE International Conference on Application of Information & Communication Technologies (2014)Google Scholar
  23. 23.
    Sheng, Y., Dementrio, L., Easley, R., et al.: A shearlet approach to edge analysis and detection. IEEE Trans. Image Process. 18(5), 929–941 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Optoelectronic Technology, School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingPeople’s Republic of China

Personalised recommendations