Abstract
Image de-noising is a very important step in Electron Microscopy (EM) image processing, before the three-dimensional reconstruction (tomography reconstruction) of the EM images. They normally have a problem of high noise level, which causes a loss in the contained information. This paper brings out the efficiency of the wavelet transform in the aim of improving the quality of real datasets. These real datasets are an EM images took at different time exposure, meaning reducing the noise level, where it seems better to answer the tradeoff between the use of Low electron doses to reduce the radiation damage, and feasibility to improve SNR after acquisition. In this matter, we have considered both hard and soft thresholding. To assess our results, we have chosen the signal-to-noise-ratio SNR criterion beside the visual quality of the obtained images. As expected, the wavelet was the right choice to perform well in Electron Microscopy and to be efficient in terms of SNR improvement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Moulin, P., Liu, J.: Analysis of multiresolution image denoising schemes using generalized gaussian and complexity priors. IEEE Inf. Theory 45(3), 909–919 (1999)
Strela, V., Portilla, J., Wainwright, M., Simoncelli, E.P.: Image denoising using gaussian scale mixtures in the wavelet domain. In: Proceedings of SPIE 45th Annual Meeting, San Diego, CA, August 2000
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Simulation 4 (2005)
Chang, S.G., Yu, B., Vetterli, M.: Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans. Image Process. 9, 1522–1531 (2000)
Guo, H., Odegard, J.E., Lang, M., Gopinath, R.A., Selesnick, I.W., Burrus, C.S.: Wavelet based speckle reduction with application to SAR based ATD/R. In: First International Conference on Image Processing, vol. 1, pp. 75–79, November 1994
Wufan, C.: Wavelet Analysis and Its Application on Image Processing. Publishing House of Science, Beijing (2011)
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)
Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)
Jansen, M., Bulthel, A.: Empirical bayes approach to improve wavelet thresholding for image noise reduction. J. Am. Stat. Assoc. 96(454), 629–639 (2001)
Boudjlel, A., Messali, Z., Boubchir, L., Chetih, N.: Non parametric bayesian estimation structure in the wavelet domain of multiple noisy image copies. In: 6th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications SITIS, 21–24 March, Sousse, Tunisia (2012)
Sousa, A.A., Kruhlak, M.J.: Nanoimaging: Methods and Protocols. Springer Protocols. Humana Press, New York (2013)
Hayat, M.A.: Principles and Techniques of Electron Microscopy: Biological Application. Cambridge University Press, Cambridge (2000)
Frank, J.: Electron Tomography. Three-Dimensional Imaging with the Transmission Electron Microscope. Plenum Press, New York (1992)
Marco, S., Boudier, T., Messaoudi, C., Rigaud, J.L.: Electron tomography of biological samples. Biochemistry (Mosc) 69(11), 1219–1225 (2004)
Messaoudi, C., Garreau de Loubresse, N., Boudier, T., Dupuis-Williams, P., Marco, S.: Multiple-axis tomography: applications to basal bodies from Paramecium tetraurelia. Biol. Cell 98, 415–425 (2006)
Boudier, T., Lechaire, J.P., Frebourg, G., Messaoudi, C., Mory, C., Colliex, C., Gaill, F., Marco, S.: A public software for energy filtering transmission electron tomography (EFTET-J): application to the study of granular inclusions in bacteria from Riftia pachyptila. J. Struct. Biol. 151(2), 151–159 (2005)
Messaoudi, C., Boudier, T., Sorzano, C., Marco, S.: TomoJ: tomography software for three-dimensional reconstruction electron microscopy. BMC Bioinform. 6(8), 288 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Soumia, S.A., Messali, Z., Ouahabi, A., Marco, S. (2017). Wavelets Based Image De-Noising: Application to EFTEM Imaging. In: Chadli, M., Bououden, S., Zelinka, I. (eds) Recent Advances in Electrical Engineering and Control Applications. ICEECA 2016. Lecture Notes in Electrical Engineering, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-319-48929-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-48929-2_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48928-5
Online ISBN: 978-3-319-48929-2
eBook Packages: EngineeringEngineering (R0)