CONFIDENCE-BASED DENOISING RELYING ON A TRANSFORMATION-INVARIANT, ROBUST PATCH SIMILARITY - Exploring Ways to Improve Patch Synchronous Summation

Cesario V. Angelino, Eric Debreuve, Michel Barlaud

Abstract

Patch-based denoising techniques have proved to be very efficient. Indeed, they account for the correlations that exist among the patches of natural images, even when degraded by noise. In this context, we propose a denoising method which tries to minimize over-smoothing of textured areas (an effect observed with NLmeans), to avoid staircase effects in monotonically varying areas (an effect observed with BM3D), and to limit spurious patterns in areas with virtually no variations. The first step of the proposed method is to perform patch denoising by averaging similar patches of the noisy image (the equivalent in the space of patches to synchronous summation for temporal signals). From there, our contribution is twofold. (a) We proposed to combine the resulting overlapping denoised patches accounting for an assessed patch denoising confidence. (b) Since a crucial aspect is the definition of a similarity between two patches, we defined a patch similarity invariant to some transformations and robust to noise thanks to a polynomial patch approximation, instead of a classical weighted L2-similarity. The experimental results show an arguably better visual quality of images denoised using the proposed method compared to NL-means and BM3D. In terms of PSNR, the results are significantly above NL-means and comparable to BM3D.

References

  1. Ahmad, I. A. and Lin, P. (1976). A nonparametric estimation of the entropy for absolutely continuous distributions. IEEE Transactions On Information Theory, pages 372-375.
  2. Angelino, C. V., Debreuve, E., and Barlaud, M. (2008). Image restoration using a knn-variant of the mean-shift. In IEEE ICIP, San Diego, CA, USA.
  3. Angelino, C. V., Debreuve, E., and Barlaud, M. (2010). Patch confidence k-nearest neighbors denoising. In IEEE ICIP, Hong Kong.
  4. Awate, S. P. and Whitaker, R. T. (2006). Unsupervised, information-theoretic, adaptive image filtering for image restoration. IEEE Trans. Pattern Anal. Mach. Intell., 28(3):364-376.
  5. Boulanger, J., Kervrann, C., and Bouthemy, P. (2007). Space-time adaptation for patch-based image sequence restoration. IEEE Trans. On Pattern Analysis And Machine Intell., 29:1096-1102.
  6. Brox, T. and Cremers, D. (2007). Iterated nonlocal means for texture restoration. In Scale Space and Variational Methods in Computer Vision, First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings, pages 13-24.
  7. Buades, A., Coll, B., and Morel, J. (2005). A non-local algorithm for image denoising. In IEEE CVPR, pages 60-65, Washington, DC, USA.
  8. Carlsson, G., Ishkhanov, T., de Silva, V., and Zomorodian, A. (2008). On the local behavior of spaces of natural images. Int. J. Comput. Vision, 76:1-12.
  9. Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24:603-619.
  10. Criminisi, A., Pérez, P., and Toyama, K. (2004). Region filling and object removal by exemplar-based image inpainting. IEEE Trans. on Image Process., 13:1200- 1212.
  11. Dabov, K., Foi, A., K., V., and Egiazarian, K. (2007). Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Trans. Image Process., 16:2007.
  12. Fukunaga, K. and Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. On Inf. Theory, 21:32-40.
  13. Huang, J. and Mumford, D. (1999). Statistics of natural images and models. In IEEE CVPR, pages 541-547.
  14. Lee, A. B., Pedersen, K. S., and Mumford, D. (2003). The nonlinear statistics of high-contrast patches in natural images. Int. J. Comput. Vision, 54(1-3):83-103.
  15. Orchard, J., Ebrahimi, M., and Wong, A. (2008). Efficient nonlocal-means denoising using the svd. In IEEE International Conference on Image Processing, pages 1732-1735, San Diego (CA), USA.
  16. Salmon, J. and Strozecki, Y. (2010). From patches to pixels in non-local methods: Weighted-average reprojection. In IEEE International Conference on Image Processing, Hong Kong, China.
  17. Sun, W., Peng, Y., and Hwang, W. (2009). Modified similarity metric for non-local means algorithm. Electronics Letters, 45(25):1307-1309.
  18. Tasdizen, T. (2008). Principal components for non-local means image denoising. In IEEE International Conference on Image Processing, pages 1728-1731, San Diego (CA), USA. Electr. & Comput. Eng. Dept., Univ. of Utah, UT.
  19. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13:600-612.
Download


Paper Citation


in Harvard Style

V. Angelino C., Debreuve E. and Barlaud M. (2011). CONFIDENCE-BASED DENOISING RELYING ON A TRANSFORMATION-INVARIANT, ROBUST PATCH SIMILARITY - Exploring Ways to Improve Patch Synchronous Summation . In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2011) ISBN 978-989-8425-46-1, pages 65-71. DOI: 10.5220/0003374400650071


in Bibtex Style

@conference{imagapp11,
author={Cesario V. Angelino and Eric Debreuve and Michel Barlaud},
title={CONFIDENCE-BASED DENOISING RELYING ON A TRANSFORMATION-INVARIANT, ROBUST PATCH SIMILARITY - Exploring Ways to Improve Patch Synchronous Summation},
booktitle={Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2011)},
year={2011},
pages={65-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003374400650071},
isbn={978-989-8425-46-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2011)
TI - CONFIDENCE-BASED DENOISING RELYING ON A TRANSFORMATION-INVARIANT, ROBUST PATCH SIMILARITY - Exploring Ways to Improve Patch Synchronous Summation
SN - 978-989-8425-46-1
AU - V. Angelino C.
AU - Debreuve E.
AU - Barlaud M.
PY - 2011
SP - 65
EP - 71
DO - 10.5220/0003374400650071