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Self-adaptive spatial image denoising model based on scale correlation and SURE-LET in the nonsubsampled contourlet transform domain

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Abstract

A novel self-adaptive image denoising model based on scale correlation and Stein’s unbiased risk estimate-linear expansion of thresholds (SURE-LET) in the nonsubsampled contourlet transform domain is proposed in this paper. First we implement the multidimensional and translation invariant decomposition for spatial images by the nonsubsampled contourlet transform, and establish the image cross-scale description structure. Then combining the scale correlation, we make improvements for the existing SURE-LET denoising idea and establish the self-adaptive denoising mechanism. The scale correlation calculation is needed for the coefficients at different scales and sub-bands to determine whether the coefficients are retained or processed with the adaptive SURE-LET threshold shrinkage. And meanwhile a new local context self-adaptive threshold strategy is proposed in the process of scale correlation calculation. Experimental results both on spatial images and standard images demonstrate that the proposed algorithm performs significantly better in terms of both the visual subjective evaluation and the quantitative objective evaluation. The method can achieve better noise suppression, and effectively retain image edge details.

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    MeiYu Liang, JunPing Du & HongGang Liu

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  1. MeiYu Liang
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  2. JunPing Du
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  3. HongGang Liu
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Correspondence to JunPing Du.

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Liang, M., Du, J. & Liu, H. Self-adaptive spatial image denoising model based on scale correlation and SURE-LET in the nonsubsampled contourlet transform domain. Sci. China Inf. Sci. 57, 1–15 (2014). https://doi.org/10.1007/s11432-013-4943-1

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  • DOI: https://doi.org/10.1007/s11432-013-4943-1

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