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access icon openaccess Image noise reduction based on applying adaptive thresholding onto PDEs methods

In this study the authors present a novel image denoising method based on applying adaptive thresholding on partial differential (PDEs) methods. In the proposed method the authors utilise the adaptive thresholding to blend the total variation filter with anisotropic diffusion filter. The adaptive thresholding has a high capacity to adapt and change according to the amount of noise. More specifically, applying a hard thresholding on the higher noise areas, whereas, applying soft thresholding on the lower noise areas. Therefore, the authors can successfully remove the noise effectively and maintain the edges of the image simultaneously. Based on the adaptation and stability of the adaptive thresholding we can achieve; optimal noise reduction and sharp edges as well. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of noise removal and edges preservation, in addition to 4.7 dB gain higher than those in the other reference algorithms.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 16. Weickert, J.: ‘Anisotropic diffusion in image processing’ (University of Copenhagen Denmark Press, 1998).
    7. 7)
      • 21. Liu, J., Gao, F., Li, Z.: ‘A model of image denoising based on partial differential equations’. Proc. of IEEE Int. Conf. on Multimedia Technology (ICMT), 2011, pp. 18921896.
    8. 8)
      • 22. Yahya, A.A., Tan, J., LI, L., et al: ‘A hybrid method of image denoising based on the isotropic diffusion and total variation models’, J. Comput. Inf. Syst., 2015, 11, (3), pp. 11491161.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 4. Bayram, I., Kamasak, M.E.: ‘Directional total variation’. Proc. of 20th European Signal Processing Conf., 2012, pp. 265269.
    14. 14)
      • 19. Andreu, F., Mazón, J.M., Moll, J.S.: ‘The total variation flow with nonlinear boundary conditions’, Asymptotic Anal., 2005, 43, (1-2), pp. 946.
    15. 15)
      • 20. Chono, K., Senda, Y.: ‘Enhanced reconstruction of AVC/H.264 intra video based on motion compensated temporal filtering and total-variation regularization’. Proc. of IEEE Picture Coding Symp., 2009, pp. 14.
    16. 16)
      • 8. Yang, Y., Li, B.: ‘Non-linear image enhancement for digital TV applications using Gabor filters’. Proc. of IEEE Int. Conf. on Multimedia and Expo, 2005, pp. 14.
    17. 17)
      • 12. Tang, C., Han, L., Ren, H., et al: ‘The oriented-couple partial differential equations for filtering in wrapped phase patterns’, Opt. Soc. Am., 2009, 17, (7), pp. 56065617.
    18. 18)
    19. 19)
      • 18. Tikhonov, A.N., Arsenin, V.Y.: ‘Solutions of ill-posed problem’ (Winston and Sons, Washington, DC, 1977).
    20. 20)
    21. 21)
      • 13. Kamalaveni, V., Rajalakshmi, R.A., Narayanankutty, K.A.: ‘Image denoising using variations of Perona-Malik model with different edge stopping functions’. Proc. of Second Int. Symp. on Computer Vision and the Internet, 2015, vol. 58, pp. 673682.
    22. 22)
      • 9. Ghael, S., Sayeed, A.M., Baraniuk, R.G.: ‘Improved wavelet denoising via empirical wiener filtering’. Proc. of SPIE, Wavelet Applications in Signal and Image Processing V, 1997, vol. 3169, pp. 389399.
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