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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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)
Luisier, F., Blu, T.: SURE-LET multichannel image denoising: interscale orthonormal wavelet thresholding. IEEE Trans. Image Process. 17(4), 482–492 (2008)
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)
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)
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)
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)
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)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenom. 60(1), 259–268 (1992)
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)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Priego, B., Duro, R.J., Chanussot, J. (2017). Cellular Automata-Based Image Sequence Denoising Algorithm for Signal Dependent Noise. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-59773-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59772-0
Online ISBN: 978-3-319-59773-7
eBook Packages: Computer ScienceComputer Science (R0)