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Image denoising using normal inverse gaussian model in quaternion wavelet domain

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Abstract

This paper proposes a novel image denoising algorithm that can more effectively remove Gaussian white noise. The proposed algorithm is based on a design of a Maximum Posteriori Estimator (MAP) combined with a Quaternion Wavelet Transform (QWT) that utilizes the Normal Inverse Gaussian (NIG) Probability Density Function (PDF). The QWT is a near shift-invariant whose coefficients include one magnitude and three phase values. An NIG PDF which is specified by four real-value parameters is capable of modeling the heavy-tailed QWT coefficients, and describing the intra-scale dependency between the QWT coefficients. The NIG PDF is applied as a prior probability distribution, to model the coefficients by utilizing the Bayesian estimation technique. Additionally, a simple and fast method is given to estimate the parameters of the NIG PDF from the neighboring QWT coefficients. Experimental results show that the proposed method outperforms other existing denoising methods in terms of the PSNR, the structural similarity, and the edge preservation. It is clear that the proposed method can remove Gaussian white noise more effectively.

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Acknowledgments

This work is partially supported by National Natural Science Foundation of China (61202319, 61272077, 61203243, 61162002); Natural Science Foundation of Jiangxi (20114BAB201034, 20122BAB211025); China Postdoctoral Science Foundation under grant No.(2013 M530223, 2013 M530224); Department of Education of Jiangxi (GJJ13481).

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  1. School of Computer Science and Engineering of Southeast University, Jiangsu, Nanjing, 211102, China

    Shan Gai & Limin Luo

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  1. Shan Gai
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  2. Limin Luo
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Correspondence to Shan Gai.

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Gai, S., Luo, L. Image denoising using normal inverse gaussian model in quaternion wavelet domain. Multimed Tools Appl 74, 1107–1124 (2015). https://doi.org/10.1007/s11042-013-1812-2

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