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

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.