Advertisement

Cellular Automata-Based Image Sequence Denoising Algorithm for Signal Dependent Noise

  • Blanca PriegoEmail author
  • Richard J. Duro
  • Jocelyn Chanussot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

This work deals with the problem of denoising sequences of multi-dimensional images that are corrupted by different types of noise. The denoising is performed through a cellular automata based filtering structure (4DCAF) that jointly considers spectral, spatial and temporal information by means of a three-dimensional neighborhood when each pixel of the sequence is processed. The novelty of the proposed method is its capacity to contemplate information concerning the type of noise by using as training data specific image sequences to tune the algorithm. The 4DCAF structures outperform selected state-of-the-art algorithms on both single band and multi-dimensional image sequences corrupted by different sources of noise.

Keywords

Cellular automata Signal dependent noise Spatio-spectro-temporal denoising Hyperspectral denoising 

Notes

Acknowledgements

This work has been partially funded by the MINECO of Spain as well as by the Xunta de Galicia and the European Regional Development Funds through grants TIN2015-63646-C5-1-R and redTEIC network (ED341D R2016/012).

References

  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65. IEEE (2005)Google Scholar
  3. 3.
    Dabov, K., Foi, A., Egiazarian, K.: Video denoising by sparse 3D transform-domain collaborative filtering. In: Proceedings of the 15th European Signal Processing Conference, vol. 1, p. 7 (2007)Google Scholar
  4. 4.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Electronic Imaging 2006, p. 606414. International Society for Optics and Photonics (2006)Google Scholar
  5. 5.
    Lam, A., Sato, I., Sato, Y.: Denoising hyperspectral images using spectral domain statistics. In: 2012 21st International Conference on Pattern Recognition, pp. 477–480. IEEE (2012)Google Scholar
  6. 6.
    Liao, C.S., Choi, J.H., Zhang, D., Chan, S.H., Cheng, J.X.: Denoising stimulated Raman spectroscopic images by total variation minimization. J. Phys. Chem. C 119(33), 19397–19403 (2015)CrossRefGoogle Scholar
  7. 7.
    Liu, X., Bourennane, S., Fossati, C.: Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis. IEEE Trans. Geosci. Remote Sens. 50(10), 3717–3724 (2012)CrossRefGoogle Scholar
  8. 8.
    Luisier, F., Blu, T.: SURE-LET multichannel image denoising: interscale orthonormal wavelet thresholding. IEEE Trans. Image Process. 17(4), 482–492 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Maggioni, M., Katkovnik, V., Egiazarian, K., Foi, A.: Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22(1), 119–133 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Peng, Y., Meng, D., Xu, Z., Gao, C., Yang, Y., Zhang, B.: Decomposable nonlocal tensor dictionary learning for multispectral image denoising. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2949–2956. IEEE (2014)Google Scholar
  11. 11.
    Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process. 12(11), 1338–1351 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Priego, B., Veganzones, M.A., Chanussot, J., Amiot, C., Prieto, A., Duro, R.J.: Spatio-temporal cellular automata-based filtering for image sequence denoising: application to fluoroscopic sequences. In: 2013 20th IEEE International Conference on Image Processing, pp. 548–552. IEEE (2013)Google Scholar
  13. 13.
    Renard, N., Bourennane, S., Blanc-Talon, J.: Denoising and dimensionality reduction using multilinear tools for hyperspectral images. Geosci. Remote Sens. Lett. 5(2), 138–142 (2008)CrossRefGoogle Scholar
  14. 14.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenom. 60(1), 259–268 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Salmon, J., Harmany, Z., Deledalle, C.A., Willett, R.: Poisson noise reduction with non-local PCA. J. Math. Imaging Vis. 48(2), 279–294 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Varghese, G., Wang, Z.: Video denoising based on a spatiotemporal Gaussian scale mixture model. IEEE Trans. Circuits Syst. Video Technol. 20(7), 1032–1040 (2010)CrossRefGoogle Scholar
  17. 17.
    Ye, M., Qian, Y., Zhou, J.: Multitask sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising. IEEE Trans. Geosci. Remote Sens. 53(5), 2621–2639 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Blanca Priego
    • 1
    Email author
  • Richard J. Duro
    • 1
  • Jocelyn Chanussot
    • 2
  1. 1.Integrated Group for Engineering ResearchUniversidade da CorunaA CoruñaSpain
  2. 2.GIPSA-LabGrenoble Institute of TechnologyGrenobleFrance

Personalised recommendations