Adaptive Fusion Based Hybrid Denoising Method for Texture Images

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

This paper presents an efficient image denoising method by adaptively combining the features of wavelets and wave atom transforms. These transforms will be applied separately on the smooth areas of the image and the texture part of the image. The disintegration of the homogenous and nonhomogenous regions of noisy image is done by decomposing the noisy image into a noisy cartoon (smooth) image and a noisy texture image. Wavelets are good at denoising the smooth regions in an image and will be used to denoise the noisy cartoon image. Wave atoms better preserve the texture in an image hence is used to denoise the noisy texture image. The two images will be fused adaptively. For adaptive fusion different weights will be chosen for different areas in the image. Areas containing higher degree of texture will be allotted more weight, while the smoother regions will be weighed lightly. The information regarding the weights selection will be obtained from the variance map of the denoised texture image. Experimental results on standard test images provide better denoising results in terms of PSNR, SSIM, FOM and UQI. Texture is efficiently preserved and no unpleasant artifacts are observed.

Keywords

Wavelet Coefficient Compressive Sensing Texture Image Noisy Image Denoising Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Po, D.D.-Y., Do, M.N.: Directional Multiscale Modeling of Images Using the Contourlet Transform. IEEE Trans. Image Processing 15(6), 1610–1620 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Starck, J.L., Candes, E.J., Donoho, D.L.: The Curvelet Transform for Image Denoising. IEEE Trans. Image Processing 11(6), 670–684 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Donoho, D.: Wedgelets: Nearly Minimax Estimation of Edges. Ann. Statistics 27(3), 859–897 (1999)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Le Pennec, E., Mallat, S.: Sparse Geometrical Image Approximation with Bandlets. IEEE Trans. Image Processing 14(4), 423–438 (2005)CrossRefGoogle Scholar
  5. 5.
    Demanet, L., Ying, L.: Wave Atoms and Sparsity of Oscillatory Patterns. Applied and Computational Harmonic Analysis 23(3), 368–387 (2007)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Swami, P.D., Jain, A., Singhai, J.: A Multilevel Shrinkage Approach for Curvelet Denoising. In: IEEE Proc. of International Conference on Information and Multimedia Technology, Jeju Island, Korea, pp. 268–272 (2009)Google Scholar
  7. 7.
    Pizurica, A., Philips, W.: Estimating the Probability of the Presence of a Signal of Interest in Multiresolution Single- and Multiband Image Denoising. IEEE Trans. Image Process. 15(3), 654–665 (2006)CrossRefGoogle Scholar
  8. 8.
    Tessens, L., Pizurica, A., Alecu, A., Munteanu, A., Philip, W.: Context Adaptive Image Denoising through Modeling of Curvelet Domain Statistics. Journal of Electronic Imaging 17(3), 033021-1—033021-17 (2008)Google Scholar
  9. 9.
    Liu, J., Moulin, P.: Information-Theoretic Analysis of Interscale and Intrascale Dependencies between Image Wavelet Coefficients. IEEE Trans. Image Processing 10(11), 1647–1658 (2001)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Nasri, M., Pour, H.N.: Image Denoising in the Wavelet Domain using a New Adaptive Thresholding Function. Elsevier J. Neurocomputing 72, 1012–1025 (2009)CrossRefGoogle Scholar
  11. 11.
    Bhutada, G.G., Anand, R.S., Saxena, S.C.: PSO-based Learning of Sub-band Adaptive Thresholding Function for Image Denoising. Signal Image and Video Processing (2010), doi:10.1007/s11760-010-0167-7Google Scholar
  12. 12.
    Elad, M., Aharon, M.: Image Denoising via Sparse and Redundant Representations Over Learned Dictionaries. IEEE Trans. Image Processing 15(12), 3736–3745 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Elad, M., Figueiredo, M.A.T., Ma, Y.: On the Role of Sparse and Redundant Representation in Image Processing. IEEE Proceedings - Special Issue on Applications of Sparse Representations and Compressive Sensing 98(6), 972–982 (2010)Google Scholar
  14. 14.
  15. 15.
    Bhutada, G.G., Anand, R.S., Saxena, S.C.: Edge Preserved Image Enhancement using Adaptive Fusion of Images Denoised by Wavelet and Curvelet Transform. Digital Signal Processing 21, 118–129 (2011)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Wang, Z., Bovik, A.C.: Universal Image Quality Index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  18. 18.
    Pratt, W.K.: Digital Image Processing, 3rd edn. John Wiley and Sons (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Samrat Ashok Technological InstituteVidishaIndia

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