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

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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.

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References

  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)

    Article  Google Scholar 

  2. Tarmissi, K., Hamza, A.B.: Multivariate kernel diffusion for surface denoising. SIViP 5(2), 191–201 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  9. Liu, J., Wang, Y., Su, K., et al.: Image denoising with multidirectional shrinkage in directionlet domain. Signal Process. 125, 64–78 (2016)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  11. Zhong, H., Ma, K., Zhou, Y.: Modified BM3D algorithm for image denoising using nonlocal centralization prior. Signal Process. 106, 342–347 (2015)

    Article  Google Scholar 

  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)

  13. Deng, C.Z.: Image Sparse Representation Theory and Its Application. M. S. thesis, Huazhong University of Science and Technology (2008)

  14. Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  Google Scholar 

  15. Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997)

    Article  MathSciNet  Google Scholar 

  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)

  17. Huggins, P.S., Zucker, S.W.: Greedy Basis Pursuit. IEEE Trans. Signal Process. 55(7), 3760–3772 (2007)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

    Article  MathSciNet  Google Scholar 

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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).

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Authors and Affiliations

  1. Department of Optoelectronic Technology, School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zhuang Zhang, Xu Chen, Lei Liu, Yefei Li & Yubin Deng

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  1. Zhuang Zhang
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  2. Xu Chen
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  3. Lei Liu
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  4. Yefei Li
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Correspondence to Lei Liu.

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Zhang, Z., Chen, X., Liu, L. et al. A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit. SIViP 14, 737–745 (2020). https://doi.org/10.1007/s11760-019-01606-1

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  • DOI: https://doi.org/10.1007/s11760-019-01606-1

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