Abstract
This paper proposes a new denoising diffusion model for fluorescence microscopic images, in which fourth-order partial differential equations (PDEs) and contrast enhancement are utilized to overcome the blocky effect and false edges usually caused by second-order PDEs. Experimental results show that the proposed method not only makes the denoised images subjectively natural and clear, but also achieves better performance in terms of objective criterion such as peak signal to noise ratio (PSNR) compared to the second-order PDEs diffusion models.
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
Preview
Unable to display preview. Download preview PDF.
References
Sarder, P., Nehorai, A.: Deconvolution methods for 3-D fluorescence microscopy images. IEEE Signal Processing Magazine 23(3), 32–45 (2006)
Jesús, V., Juan, A.Q., Ismael, R.: Regularized quadratic cost function for oriented fringe-pattern filtering. Optics Letters 34(11), 1741–1743 (2009)
Tang, C., Zhang, F., Li, B.T., Yan, H.Q.: Performance evaluation of partial differential equation models in electronic speckle pattern interferometry and the mollification phase map method. Applied Optics 45(28), 7392–7400 (2006)
Witkin, A.P.: Scale-space filtering. In: Proc. Int. Joint Conf. on Artificial Intelligence, Karlsruhe, vol. 2, pp. 1019–1021 (1983)
Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Trans. on PAMI 12(7), 629–639 (1990)
Zhang, H.M., Luo, Q.M., Zeng, S.Q.: Restoration of fluorescence images from two-photon microscopy using modified nonlinear anisotropic diffusion filter. In: Proc. Int. Conf. on SPIE, Wuhan, vol. 6534, pp. 6534H-1–6534H-8 (2006)
Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992)
Alvarez, L., Lions, P.L., Morel, J.M.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(3), 845–866 (1992)
You, Y.L., Kaveh, M.: Fourth-order partial differential equations for noise removal. IEEE Trans. on Image Processing 9(10), 1723–1730 (2000)
Rivera, M., Marroquin, J.L.: Efficient half-quadratic regularization with granularity control. Image Vis. Comput. 21, 345–357 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Wang, Y., Xue, H. (2012). Applying Fourth-Order Partial Differential Equations and Contrast Enhancement to Fluorescence Microscopic Image Denoising. In: Tan, H. (eds) Knowledge Discovery and Data Mining. Advances in Intelligent and Soft Computing, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27708-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-27708-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27707-8
Online ISBN: 978-3-642-27708-5
eBook Packages: EngineeringEngineering (R0)