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  • Volume 2017

    Reduction of Noise from Fingerprint Images using Stationary Wavelet Trasnform
    (International Journal of Engineering Works)

    Vol. 4, Issue. 12, PP. 104-108, December 2017
    Keywords: AFIS, Fingerprints, De-noising, Wavelets, Noise

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    In Automatic Fingerprint Identification Systems (AFIS) the quality of image is a very important factor as the minutiae extraction from fingerprint image heavily depends on image quality. To enhance the quality of fingerprint images a large number of denoising methods has been used. In this paper fingerprint image enhancement using stationary wavelet transform has been analyzed using different wavelets with different thresholds. Four different wavelets namely Haar DB4 (Daubechies), Coif2 (Coilflets) and Bior1.3 (Biorthogonal) were selected with four thresholds namely VisuShrink, NormalShrink, NeighShrink and BaysShrink. The methods were applied on three types of noises which were Speckle noise, Gaussian noise and Salt and Pepper noise. The effect of changing decomposition level on noise removal efficiency based on PSNR (Peak Signal to Noise Ratio).


    1. Nasar Iqbal is Research Scholar in Department of Electrical Engineering, University of Engineering & Technology Peshawar, Pakistan.

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    Nasar Iqbal, "Reduction of Noise from Fingerprint Images using Stationary Wavelet Trasnform" International Journal of Engineering Works, Vol. 4, Issue. 12, PP. 104-108, December 2017.


    1. [1] Hong, Lin, Yifei Wan, and Anil Jain. ”Fingerprint image enhancement:Algorithm and performance evaluation.” IEEE transactions on patternanalysis and machine intelligence 20.8 (1998)
    2. [2] Greenberg, Shlomo, et al.”Fingerprint image enhancement using filtering echniques.” Pattern Recognition, 2000. Proceedings. 15th InternationalConference on. Vol. 3. IEEE, 2000.
    3. [3] Kale, K. V., R. R. Manza, S. S. Gornale, P. D. Deshmukh, andVikasHumbe. ”SWT Based composite method for fingerprint imageenhancement.” In Signal Processing and Information Technology, 2006IEEE International Symposium on, pp. 162-167. IEEE, 2006.
    4. [4] Sherlock, B. G., D. M. Monro, and K. Millard. ”Fingerprint enhancementby directional Fourier filtering.” IEE Proceedings-Vision, Imageand Signal Processing 141.2 (1994): 87-94.
    5. [5] Kim, Byung-Gyu, Han-Ju Kim, and Dong-Jo Park. ”New enhancementalgorithm for fingerprint images.” In Pattern Recognition, 2002. Proceedings.16th International Conference on, vol. 3, pp. 879-882. IEEE,2002.
    6. [6] Babatunde, Iwasokun Gabriel, Alese Boniface Kayode, AkinyokunOluwole Charles, and OlabodeOlatubosun. ”Fingerprint image enhancement:Segmentation to thinning.” (2012).
    7. [7] Bentley, Paul M., and J. T. E. McDonnell. ”Wavelet transforms: anintroduction.” Electronics communication engineering journal 6, no.4 (1994): 175-186.
    8. [8] Ruikar, Sachin, and D. D. Doye. ”Image denoising using wavelettransform.” Mechanical and Electrical Technology (ICMET), 2010 2ndInternational Conference on. IEEE, 2010.
    9. [9] Yinping, M., Yongxing, H. (2012, March). Adaptive threshold basedon wavelet transform fingerprint image denoising. In Computer Scienceand Electronics Engineering (ICCSEE), 2012 International Conferenceon (Vol. 3, pp. 494-497). IEEE.
    10. [10] D. L. Donoho and I. M. Johnstone, ”Denoising by soft thresholding”,IEEE Trans. on Iriform. Theory, Vol. 41, pp. 613-627, 1995.
    11. [11] T. D. Bui and G. Y. Chen, ”Translation-invariant denoising usingmultiwavelets”, IEEE Transactions on Signal Processing, Vo1.46, No.l2,pp.3414-3420, 1998.
    12. [12] D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation via waveletshrinkage, Biomefrika, vol. 81, pp. 425455, 1994.