Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency

Authors:
Ying Chen;Zhicheng Ji;Chunjian Hua
Affiliations:
Research Center of Control Science and Engineering, School of Communication and Control Engineering, Southern Yangtze University, Wuxi, China 214122;Research Center of Control Science and Engineering, School of Communication and Control Engineering, Southern Yangtze University, Wuxi, China 214122;School of Mechanical Engineering, Southern Yangtze University, Wuxi, China 214122
Venue:
Multidimensional Systems and Signal Processing
Year:
2008

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Abstract

Threshold selection is critical in image denoising via wavelet shrinkage. Many powerful approaches have been investigated, but few of them are adaptive to the changing statistics of each subband and meanwhile keep efficiency of the algorithm. In this work, an inter-scale adaptive, data-driven threshold for image denoising via wavelet soft-thresholding is proposed. To get the optimal threshold, a Bayesian estimator is applied to the wavelet coefficients. The threshold is based on the accurate modeling of the distribution of wavelet coefficients using generalized Gaussian distribution (GGD), and the near exponential prior of the wavelet coefficients across scales. The new approach outperforms BayesShrink because it captures the statistical inter-scale property of wavelet coefficients, and is more adaptive to the data of each subband. The simplicity of the proposed threshold makes it easy to achieve the spatial adaptivity, which will further improves the wavelet denoising performance. Simulation results show that higher peak-signal-to-noise ratio can be obtained than other thresholding methods for image denoising.