Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

DOI: 10.4236/jsip.2013.43B026   PDF   HTML     2,943 Downloads   4,025 Views   Citations


The de-noising of the fingerprint image is one of the key tasks before the extraction of the minutiae in automatic fingerprint matching. When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local minutiae in the fingerprint image very well. To address this problem, we propose a local orientation field based nonlocal means (NLM-LOF) method in this paper. Experimental results on the simulated and real images show that the proposed method can suppress noise effectively while preserving edges and details in the fingerprint image and it outperforms the state-of-art nonlocal means method in terms of qualitative metrics and visual comparisons.


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J. Zou, J. Feng, X. Zhang and M. Ding, "Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 150-153. doi: 10.4236/jsip.2013.43B026.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] L. Hong, Y. F. Wan and A. Jain, “Fingerprint Image Enhancement Algorithm and Performance Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, August 1998.
[2] Y. Y. Liang and P. Z. Wen, “Fingerprint Image Denoising using Morphological Amoebas,” International Conferences on Intelligent Computing and Integrated Systems (ICISS), 2010, pp.197-200.
[3] X. F. Liang and T. ASANO, “A Linear Time Algorithm for Binary Fingerprint Image Denoising Using Distance Transform,” IEICE TRANS. INF. & SYST., Vol. E89-D, No.4, April 2006.
[4] Portilla, J. Strela, V. Wainwright and M. J. Simoncelli, “Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain,” IEEE Transactions on Image Processing, Vol. 12, No. 11, 2003, pp. 1338-1351. doi:10.1109/TIP.2003.818640
[5] A. Buades, B. Coll and J. Morel, “A Nonlocal Algorithm for Image Denoising,” IEEE International Conferences on Computer Vision and Pattern Recognition (CVPR), Vol. 2, 2005, pp. 60-65.
[6] J. Salmon and Y. Strozecki, “From Patches to Pixels in Non-Local methods:Weighted-Average Reprojection,” International Conferences On Image Processing (ICIP), 2010, pp. 1929-1932.
[7] C.-A. Deledalle J. Salmon and V. Duval, “Non-Local Methods with Shape-Adaptive Patches (NLM-SAP),” Journal of Mathematical Imaging and Vision (JMIV), Vol. 43, 2012, pp. 103-120. doi:10.1007/s10851-011-0294-y
[8] Z. Wang, A. C. Bovik, H. R. Sheik and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, Vol. 13, No. 4, Apr. 2004, pp. 600-602. doi:10.1109/TIP.2003.819861

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