Three-stage Unstructured Filter for Removing Mixed Gaussian plus Random Impulse Noise

Fitri Utaminingrum, Keiichi Uchimura, Gou Koutaki

2014

Abstract

Digital image processing is often contaminated by more than one type of noise, such as mixed noise. In this paper, we propose a three-stage process to develop K-SVD method not only for reducing Gaussian noise but also for mixed Gaussian and impulse noise with optimizing input system and preserving edge structure. A three-stage process is combining of impulse noise removal, edge reconstruction and image smoothing. Pressing of an impulse noise in the early stages by Decision Based Algorithm (DBA) and repairing edge structure by an edge-map are able to optimize the performance of the K-SVD method for smoothing an image. The performance of the filter is analysed in terms of Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity (MSSIM) index and Blind Image Quality Index (BIQI). The simulation result is obtained a significant improvement over the previous research.

References

  1. Anush, K. and Alan, C. (2010). A two-step framework for constructing blind image quality indices. Int.J IEEE Signal Processing Letters, 17(5):513-516.
  2. Astola, J. and Kuosmanen., P. (1997). Fundamental of nonlinear digital filtering. CRC Press, Boca Raton, FL. United States of America.
  3. Bogdan, S. (2010). Peer group switching filter for impulse noise reduction in color images. Int. J. Pattern Recognition Letters, 133:484-495.
  4. Buades, A., Coll, B., and Morel, J. (2005). A review of image denoising algorithms with a new one. Multiscale Modelling Simulation, 4:490-530.
  5. Chan, T., Esedoglu, S., Park, F., and Yip, M. (2005). Recent developments in total variation image restoration. Mathematical Models of Computer Vision.
  6. Church, J., Yixin, C., and Rice, S. (2008). A spatial median filter for noise removal in digital images. IEEE Southeastcon, pages 618-623.
  7. Fitri, U., Keichi, U., and Gou, K. (2012a). High density impulse noise removal by fuzzy mean linear aliasing window kernel. IEEE International Conference Signal Processing Communication and Computing, pages 711-716.
  8. Fitri, U., Keichi, U., and Gou, K. (2012b). Optimization gaussian noise removal using hybrid filter based on mean impulse fuzzy and fuzzy aliasing filter methods. IEEJ Transactions on Electronics, Information and Systems, 133(1):150-158.
  9. Jian, F., Raymond, H., and Mila, N. (2008). Two-phase methods for deblurring images corrupted by impulse plus gaussian noise. Inverse Problem Imaging, pages 187-204.
  10. Jian, F., Raymond, H., and Mila, N. (2010). Fast twophase image deblurring under impulse noise. Journal of Mathematical Imaging and Vision, 36:46-53.
  11. Laskar, R., Bhowmicks.B., Biswas.R., and Kar, S. (2009). Removal of impulse noise from color image. IEEE Region 10 TENCON, pages 1-5.
  12. Lezoray, O., Ta, V., and Elmoataz, A. (2008). Impulse noise spectral clustering and regulation on graph. IEEE International Conference on Pattern Recognition, pages 1-4.
  13. Michael, E. and Michal, A. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image ProcessingIEEE Transactions on Image Processing, 15(12):3736-3745.
  14. Michal, A. and Michael, E. (2006). Alfred,b.:k-svd an algorithm for denoising overcomplete dictionaries for sparse representation. IEEE Transactions on Image Processing, 54(11):4311-4322.
  15. Tony, F. and Ke, C. (2006). An optimization-based multilevel algorithm for total variation image denoising. SIAM Journal of Multi scale Modelling and Simulation, 5:615-645.
  16. Veerakumar, T., Esakkirajan, S., and Ila., V. (2013). Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images. In Press Int. J. Electron. Commun. (AEU).
  17. Wenbin, L. (2007). An efficient algorithm for the removal of impulse noise from corrupted images. Int. J. Electron. Commun. (AEU), 61:551-555.
  18. Yingyue, Z., Zhongfu, Y., and Yao, X. (2013). A restoration algorithm for images contaminated by mixed gaussian plus random-valued impulse noise. Int.J. Vis Commun. Image Representation, 24:283-294.
  19. Zhou, W., Bovik, A.C Sheikh, H., and Simoncelli, E. (2004). Image quality assessment: From error measurement to structural similarity. Int.J IEEE Image Processing, 13:600-612.
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Paper Citation


in Harvard Style

Utaminingrum F., Uchimura K. and Koutaki G. (2014). Three-stage Unstructured Filter for Removing Mixed Gaussian plus Random Impulse Noise . In Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014) ISBN 978-989-758-046-8, pages 99-106. DOI: 10.5220/0005051400990106


in Bibtex Style

@conference{sigmap14,
author={Fitri Utaminingrum and Keiichi Uchimura and Gou Koutaki},
title={Three-stage Unstructured Filter for Removing Mixed Gaussian plus Random Impulse Noise},
booktitle={Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)},
year={2014},
pages={99-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005051400990106},
isbn={978-989-758-046-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2014)
TI - Three-stage Unstructured Filter for Removing Mixed Gaussian plus Random Impulse Noise
SN - 978-989-758-046-8
AU - Utaminingrum F.
AU - Uchimura K.
AU - Koutaki G.
PY - 2014
SP - 99
EP - 106
DO - 10.5220/0005051400990106