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Image denoising using local adaptive layered Wiener filter in the gradient domain

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Abstract

This paper presents a new image denoising algorithm. Our method is inspired by locally adaptive window-based denoising using maximum likelihood (LAWML). In the research, we find, as with wavelet coefficients, the gradient image coefficients can also be modeled as zero-mean Gaussian random variables with high local correlation. So, we implement the local adaptive Wiener filter in the gradient domain. But unlike LAWML, the layered denoising is adopted in our method. At the same time, the relation between wavelet-based and diffusion-based denoising method is disclosed further. The tests demonstrate the proposed method gets the desired results both subjectively and objectively compared to the related gradient domain algorithms and wavelet-based image denoising methods. At the same time, the tests also show the proposed method outperforms some other diffusion filters and wavelet-based methods and Non-Local means (NL-means) filter in most cases.

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Acknowledgments

This work is partially supported by the National Nature Science Foundation of China (Grant Nos. 61102018 and 61271294) and Natural Science Foundation of Shaanxi Province (No. 2014JM8312) and Natural Science Foundation of Xianyang Normal University (No. 11XSYK304).

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Authors and Affiliations

  1. Institute of Graphics and Image Processing, Xianyang Normal University, Xianyang, 712000, China

    Xiaobo Zhang

  2. Department of Mathematics, Xidian University, Xi’an, 710071, China

    Xiangchu Feng

Authors
  1. Xiaobo Zhang
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  2. Xiangchu Feng
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Correspondence to Xiaobo Zhang.

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Zhang, X., Feng, X. Image denoising using local adaptive layered Wiener filter in the gradient domain. Multimed Tools Appl 74, 10495–10514 (2015). https://doi.org/10.1007/s11042-014-2182-0

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  • DOI: https://doi.org/10.1007/s11042-014-2182-0

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