Image denoising based on the symmetric normal inverse Gaussian model and non-subsampled contourlet transform

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Abstract
In this study, an adaptive image denoising method is proposed based on the symmetric normal inverse Gaussian (SNIG) model and the non-subsampled contourlet transform (NSCT). In the framework of Bayesian maximum a posteriori estimation, the problem of denoising is reduced to a procedure of thresholding. A novel strategy is then proposed to determine the threshold that is not only adaptive to different directions and scales, but also able to take into considerations the scale-to-scale difference in the contribution of the NSCT coefficients to the noise. The experimental results in different kinds of sample images show that the authors' method can not only result in higher peak-signal-to-noise ratio values, but also have better visual effects in reduced processing artefacts and preserved edges.