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Image Denoising Method Based on Weighted Total Variational Model with Edge Operator

  • Hong Zhang
  • Xiaoli Zhou
  • Weixiao Zhan
  • Fuhua Yu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

In order to eliminate image noise effectively, the weighted total variation algorithm based on edge detection is proposed. By calculating the amplitude of the edge operator of the image, accurate estimates of edge weights are achieved, and then the weight of the canny operator is used to weigh the Lagrangian multiplier, which is no longer a global variable, so that the filter has a better edge protection feature. Theoretical analysis and experimental results show that the method can remove noise while preserving the edge details of the image more completely. The step effects of the total variation model is effectively suppressed, and has a better performance in terms of structural similarity and the visual effect of image.

Keywords

Image denoising Weighted total variation model Canny Structural similarity 

Notes

Acknowledgements

This work was supported by the Shaanxi Natural Science Foundation (2016JM8034) and Scientific research plan projects of Henan Education Department (12JK0791).

References

  1. 1.
    Wang, Z., Hou, G., Pan, Z., Wang, G.: Single image dehazing and denoising combining dark channel prior and variational models. IET Comput. Vis. 12(11), 393–402 (2018).  https://doi.org/10.1049/iet-cvi.2017.0318CrossRefGoogle Scholar
  2. 2.
    Vazquez-Corral, J., Bertalmío, M.: Angular-based preprocessing for image denoising. IEEE Signal Process. Lett. 25(11), 219–223 (2018).  https://doi.org/10.1109/LSP.2017.2777147CrossRefGoogle Scholar
  3. 3.
    Dou, Z., Song, M., Gao, K., Jiang, Z.: Image smoothing via truncated total variation. IEEE Access 5(11), 27337–27344 (2017).  https://doi.org/10.1109/access.2017.2773503CrossRefGoogle Scholar
  4. 4.
    Habib, W., Sarwar, T., Siddiqui, A.M., Touqir, I.: Wavelet denoising of multiframe optical coherence tomography data using similarity measures. IET Image Proc. 11(11), 64–79 (2017).  https://doi.org/10.1049/iet-ipr.2016.0160CrossRefGoogle Scholar
  5. 5.
    Kwon, S., Lee, H., Lee, S.: Image enhancement with Gaussian filtering in time-domain microwave imaging system for breast cancer detection. Electron. Lett. 52(5), 342–344 (2016).  https://doi.org/10.1049/el.2015.3613CrossRefGoogle Scholar
  6. 6.
    Zhang, S., Li, X., Zhang, C.: Modified adaptive median filtering. In: 2018 International Conference on Intelligent Transportation. Big Data & Smart City (ICITBS), Xiamen (2018).  https://doi.org/10.1109/icitbs.2018.00074
  7. 7.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990).  https://doi.org/10.1109/34.56205CrossRefGoogle Scholar
  8. 8.
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica Section D. 60, 259–268 (1992).  https://doi.org/10.1016/0167-2789(92)90242-FMathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Hu, Y., Zhong, C.X., Cao, M.Y., Zhao, G.S.: A Fast High order Total variational Image denoising method based on augmented Lagrangian multiplier. Syst. Eng. Electron. Technol. 39 (12): 2831–2839 (2017).  https://doi.org/10.3969/j.issn.1001-506x.2017.12.29
  10. 10.
    Said, B.A., Foufou, S.: Modified total variation regularization using fuzzy complement for image denoising. In: 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, pp. 1–6 (2015)  https://doi.org/10.1109/ivcnz.2015.7761561
  11. 11.
    Zhao, Y., Liu, G.J., Zhang, B., Hong, W., Wu, Y.R.: Adaptive total variation regularization based SAR image despeckling and despeckling evaluation index. IEEE Trans. Geosci. Remote Sens. 53(5), 2765–2774 (2015).  https://doi.org/10.1109/tgrs.2014.2364525CrossRefGoogle Scholar
  12. 12.
    Yan, N.L., Jin, C.: Total variation image denoising model based on weighting function. Electron. Measur. Technol. 41(07), 58–63 (2018).  https://doi.org/10.19651/j.cnki.emt.1701305CrossRefGoogle Scholar
  13. 13.
    Mallick, A., Chaudhuri, S.S., Roy, S.: Optimization of Laplace of Gaussian (LoG) filter for enhanced edge detection: new approach. In: Proceedings of the 2014 International Conference on Control. Instrumentation, Energy and Communication (CIEC), pp. 658–661 (2014).  https://doi.org/10.1109/ciec.2014.6959172
  14. 14.
    Amara, B.A., Pissaloux, E., Atri, M.: Sobel edge detection system design and integration on an FPGA based HD video streaming architecture. In: 2016 11th International Design & Test Symposium (IDT), Hammamet, pp. 160–164 (2016).  https://doi.org/10.1109/idt.2016.7843033
  15. 15.
    Baştan, M., Bukhari, S.S., Breuel, T.: Active Canny: edge detection and recovery with open active contour models. IET Image Proc. 11(12), 1325–1332 (2017).  https://doi.org/10.1049/iet-ipr.2017.0336CrossRefGoogle Scholar
  16. 16.
    Ao, J.S., Zong, K., Ma, C.B.: Underwater image enhancement algorithm based on weighted guided filtering. J. Guilin Univ. Electron. Sci. Technol. 36(02), pp. 113–117 (2016).  https://doi.org/10.16725/j.cnki.cn45-1351/tn.2016.02.006

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hong Zhang
    • 1
  • Xiaoli Zhou
    • 1
  • Weixiao Zhan
    • 2
  • Fuhua Yu
    • 1
  1. 1.Xi’an University of Post and TelecommunicationsXi’anChina
  2. 2.China Academy of Information and Communications TechnologyBeijingChina

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