Using bivariate threshold function for image denoising in NSCT domain

As the main prevailing denoising method, how the threshold function works and what's the threshold value are the greatest importance techniques. Consider the dependencies between the coefficients and their parents, a non-Gaussian bivariate distribution is given, and corresponding nonlinear threshold function is derived from the model using Bayesian estimation theory. According to non-subsampled Contourlet transform and bivariate threshold function, a novel Non-Subsampled Contourlet Transform based on Bivariate threshold function (NSCTBI) for image denoising is proposed. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. To evaluate the performance of the proposed algorithms, the results are compared with existent algorithms, such as non-subsampled Contourlet transform and wavelet-based bivariate threshold function method for image denoising. The simulation results indicate that the proposed method outperforms the others 0.5-2.3 dB in PSNR, and keep better visual result in edges information reservation as well.