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An Improved Denoising Method Based on Wavelet Transform for Processing Bases Sequence Images

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

In this article, we present an improved images denoising method for base sequence images. It is based on the multiscale analysis of the images resulting from the à trous wavelet transform decomposition. We define a new thresholding function and use it to improve the denoising performance of the isotropic undecimated wavelet transform (IUWT). The proposed method selects the best suitable wavelet function based on IUWT. The advantages of the new thresholding function are that it is more robust than previous thresholding function, and the convergence of function is more efficient. The experimental results indicate that the proposed method can obtain higher signal-to-noise ratio (SNR) and mean squared error ratio (MSE) than conventional wavelet thresholding denoising methods.

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References

  1. Hémar, A., Olivo, J., Williamson, E., Saffrich, R., Dotti, C.: Dendroaxonal transcytosis of transferrin in cultured hippocampal and sympathetic neurons. J. Neurosci. 17(23), 9026–9034 (1997)

    Google Scholar 

  2. Dumenil, G., Olivo, J., Pellegrini, S., Fellous, M., Sansonetti, P., Van Nhieu, T.G.: Interferon α inhibits a Src-mediated pathway necessary for shigella-induced cytoskeletal rearrangements in Epithelial cells. J. Cell Biol. 143(4), 1003–1012 (1998)

    Article  Google Scholar 

  3. Smal, I., Loog, M., Niessen, W., Meijering, E.: Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE Trans. Med. Imaging 29(2), 282–301 (2010)

    Article  Google Scholar 

  4. Matheron, G., Serra, J.: Image Analysis And Mathematical Morphology. Academic Press, London (1982)

    Google Scholar 

  5. Bright, D., Steel, E.: Two-dimensional top hat filter for extracting spots and spheres from digital images. J. Microsc. 146(2), 191–200 (1987)

    Article  Google Scholar 

  6. Liang, L., Xu, Y., Shen, H., Camilli, P.D., Toomere, D., Duncan, J.: Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), pp. 161–166 (2012)

    Google Scholar 

  7. Eils, R., Athale, C.: Computational imaging in cell biology. J. Cell Biol. 161(3), 477–481 (2003)

    Article  Google Scholar 

  8. Zhou, X., Wong, S.T.C.: Informatics challenges of high-throughput microscopy. IEEE Sig. Proc. Mag. 23(3), 63–72 (2006)

    Article  Google Scholar 

  9. Olivo-Marin, J.: Extraction of spots in biological images using multiscale products. Pattern Recogn. 35(2), 1989–1996 (2002)

    Article  Google Scholar 

  10. Xu, Y., Weaver, J., Healy Jr, D.M., Lu, J.: Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Trans. Image Proc. 3(6), 747–758 (1994)

    Article  Google Scholar 

  11. Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992)

    Article  Google Scholar 

  12. Starck, J., Murtagii, F., Bijaoui, A.: Multiresolution support applied to image filtering and restoration. Graph. Models Image Proc. 57(5), 420–431 (1995)

    Article  Google Scholar 

  13. Murtagh, F., Starck, J.: Image processing through multiscale analysis and measurement noise modeling. Stat. Comput. 10(2), 95–103 (2000)

    Article  Google Scholar 

  14. Starck, J., Murtagh, F., Bijaoui, A.: Image Processing and Data Analysis: The Multiscale Approach. Cambridge University Press, Cambridge (1998)

    Book  MATH  Google Scholar 

  15. Sadler, B.M., Swami, A.: Analysis of multiscale products for step detection and estimation. IEEE Trans. Inf. Theo. 45(3), 1043–1051 (1999)

    Article  MathSciNet  Google Scholar 

  16. Robert, C.: The Bayesian Choice: A Decision Theoretic Motivation. Springer Texts in Statistics, New York (1994)

    Book  Google Scholar 

  17. Smal, I., Draegestein, K., Galjart, N., Niessen, W., Meijering, E.: Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: application to microtubule growth analysis. IEEE Trans. Med. Imaging 27(6), 789–804 (2008)

    Article  Google Scholar 

  18. Smal, I., Meijering, E., Draegestein, K., Galjart, N., Grigoriev, I., Akhmanova, A., Royen, M., Houstsmuller, A., Niessen, W.: Multiple object tracking in molecular bioimaging by rao-blackwellized marginal particle filtering. Med. Image Anal. 12(6), 764–777 (2008)

    Article  Google Scholar 

  19. Reddy, G., Muralidhar, M., Varadarajan, S.: ECG de-noising using improved thresholding based on wavelet transforms. IJCSNS 9(9), 221 (2009)

    Google Scholar 

  20. Donoho, D., Johnstone, J.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  Google Scholar 

  21. Chen, G., Qian, S.: Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 49(3), 973–980 (2011)

    Article  Google Scholar 

  22. Parrilli, S., Poderico, M., Angelino, C., Verdoliva, L.: A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012)

    Article  Google Scholar 

  23. Murtagh, F., Starck, J.: Image processing through multiscale analysis and measurement noise modeling. Stat. Comput. 10(2), 95–103 (2000)

    Article  Google Scholar 

  24. Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, Ph: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes, J.-M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets. Inverse Problems and Theoretical Imaging, pp. 286–297. Springer, Heidelberg (1990)

    Chapter  Google Scholar 

  25. Starck, J., Murtagii, F., Bijaoui, A.: Multiresolution support applied to image filtering and restoration. Graph. Models Image Proc. 57(5), 420–431 (1995)

    Article  Google Scholar 

  26. Jansen, M., Bultheel, A.: Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise. IEEE Trans. Image Proc. 8(7), 947–953 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  27. Ma, Z., Wen, J., Liu, Q., Tuo, G.: Near-infrared and visible light image fusion algorithm for face recognition. J. Modern Optics. 62(9), 745–753 (2015)

    Article  Google Scholar 

  28. Whiteford, N., Skelly, T., Curtis, C., Ritchie, M., Lohr, A., Zaranek, A., Abnizova, I., Brown, C.: Swift: primary data analysis for the illumina Solexa sequencing platform. Bioinf. 25(7), 2194–2199 (2009)

    Article  Google Scholar 

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Acknowledgement

This work was supported by Shenzhen Municipal Science and Technology Innovation Council (Grant No. JCYJ20130329151843309, Grant No. CXZZ20140904154910774, Grant No.JCYJ20140417172417174) and China Postdoctoral Science Foundation funded project (Grant No.2014M560264).

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Authors and Affiliations

  1. Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Ke Yan, Jin-Xing Liu & Yong Xu

Authors
  1. Ke Yan
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  2. Jin-Xing Liu
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  3. Yong Xu
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Corresponding author

Correspondence to Ke Yan .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Yan, K., Liu, JX., Xu, Y. (2015). An Improved Denoising Method Based on Wavelet Transform for Processing Bases Sequence Images. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_35

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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