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Image Adaptive Denoising Based on Nonsubsampled Contourlet Transform

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Abstract

In this paper, researching the correlation property of congenetic subband coefficients of Nonsubsampled Contourlet Transform (NSCT), and proposing a new adaptive denoising theory. This theory combines the excellent shift-invariant of NSCT with the correlation property of congenetic subband coefficients in image denoising. The correlation property contains the entire relational correlation value of the congenetic subband coefficients, and the grey correlation value in single coefficient of the congenetic subband coefficients of the NSCT. According to the correlation property of congenetic subband coefficients, the new algorithm can automatically identify the strong edges, weak edges and noise in the noisy image, and then it can filter the noise and preserve the strong edges and weak edges at certain degree. The experiment results are provided to compare with the elegant non-local means method, which show that the theory proposed in this paper has good effect.

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Acknowledgements

The authors acknowledge the Fundamental Research Funds for the Central Universities (Grant: 2572018BF05), Special Funds for Scientific and Technological Innovation Talents of Harbin (Grant: 2014RFQXJ127) and Financial assistance from postdoctoral scientific research developmental fund of Heilongjiang Province (Grant: LBH-Q14006).

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Northeast Forestry University, Harbin, China

    Peng Wu & Baokun Wang

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  1. Peng Wu
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  2. Baokun Wang
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Correspondence to Peng Wu.

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Wu, P., Wang, B. Image Adaptive Denoising Based on Nonsubsampled Contourlet Transform. Wireless Pers Commun 103, 761–772 (2018). https://doi.org/10.1007/s11277-018-5475-1

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  • DOI: https://doi.org/10.1007/s11277-018-5475-1

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