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

  • Authors:

  • Xiang-Yang Wang;Li Zhao;Pan-Pan Niu;Zhong-Kai Fu

  • Affiliations:

  • School of Computer and Information Technology, Liaoning Normal University, Dalian, China 116029;School of Computer and Information Technology, Liaoning Normal University, Dalian, China 116029;School of Computer and Information Technology, Liaoning Normal University, Dalian, China 116029 and Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Edu ...;School of Computer and Information Technology, Liaoning Normal University, Dalian, China 116029

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2011

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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.