Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (22): 4094-4097.doi: 10.3969/j.issn.1673-8225.2011.22.025

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Comparison of microscopy image denoising effects based on contourlet, curvelet and wavelet transform

Tang Min1, Chen Feng 2   

  1. 1School of Electronics and Information, Nantong University, Nantong  226007, Jiangsu Province, China; 2School of Electrical Engineering, Nantong University, Nantong  226007, Jiangsu Province, China
  • Received:2010-12-17 Revised:2011-02-10 Online:2011-05-28 Published:2011-05-28
  • About author:Tang Min☆, Doctor, Associate professor, School of Electronics and Information, Nantong University, Nantong 226007, Jiangsu Province, China tangmnt@yahoo.com.cn
  • Supported by:

    the National Natural Science Foundation of China, No. 61005054*; the Natural Science Foundation of Jiangsu Universities, No. 09KJD510004*, 10KJB510020*; Science and Technology Program of Nantong City, No. K2009032*; Science and Technology Research Start-up Foundation for Doctors in Nantong University, No. 08B15*

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Abstract:

BACKGROUND: Wavelets in two-dimension are good at isolating the discontinuities at edge points, but not the smoothness along the contours. In addition, separable wavelets only capture limited directional information, which weaken their application effects on image processing. Image multiscale geometric analysis theory is developed gradually to overcome the shortcomings of wavelets mentioned above.
OBJECTIVE: To compare the microscopy image denoising effects qualitatively and quantitatively based on contourlet, curvelet and wavelet transforms.
METHODS: Based on the brief descriptions of contourlet, curvelet and wavelet transform, performance analysis and comparison were done depending on image denoising with qualitative and quantitative indices computed, e.g., mean square error and peak signal-to-noise ratio.
RESULTS AND CONCLUSION: Experimental results demonstrate that for the test Lena images and microscopy images, curvelet transform achieves the best result, while wavelet transform result is poor.

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