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Image Denoising Using Gaussian Scale Mixtures with Gaussian–Hermite PDF in Steerable Pyramid Domain

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Abstract

In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image denoising. Its strength relies on providing a simple and, yet, very effective local statistical description of coefficient neighborhoods via a GSM vector. Combining with Bayes least squares estimator, we describe a method for removing noise from digital images, based on GSM with Gaussian–Hermite PDF in Steerable pyramid domain in this paper, which can be seen a modified version of the BLS-GSM. By introducing the Gaussian–Hermite PDF, we model the distribution of Steerable pyramid coefficients with GSM. The statistical model is then used to obtain the denoised coefficients from the noisy image decomposition by Bayes least squares estimator. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, No. 850, Huanghe Road, Shahekou District, Dalian, 116029, China

    Xiang-Yang Wang, Li Zhao, Pan-Pan Niu & Zhong-Kai Fu

  2. Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian, 116622, China

    Pan-Pan Niu

Authors
  1. Xiang-Yang Wang
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  2. Li Zhao
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  3. Pan-Pan Niu
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  4. Zhong-Kai Fu
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Corresponding author

Correspondence to Xiang-Yang Wang.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 60773031 & 60873222; the Open Foundation of State Key Laboratory of Networking and Switching Technology of China under Grant No. SKLNST-2008-1-01; the Open Foundation of Network and Data Security Key Laboratory of Sichuan Province; the Open Foundation of Key Laboratory of Modern Acoustics Nanjing University under Grant No. 08-02; the Open Foundation of Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education; and Liaoning Research Project for Institutions of Higher Education of China under Grant No. L2010230 & 2008351.

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Wang, XY., Zhao, L., Niu, PP. et al. Image Denoising Using Gaussian Scale Mixtures with Gaussian–Hermite PDF in Steerable Pyramid Domain. J Math Imaging Vis 39, 245–258 (2011). https://doi.org/10.1007/s10851-010-0238-y

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