A New Method for Denoising of Images in the Dual Tree Complex Wavelet Domain

In this paper, a new dual tree complex wavelet transform-based Bayesian method is proposed for denoising of images corrupted by additive white Gaussian noise. The symmetric normal inverse Gaussian distribution is used to model the real and imaginary parts of the complex wavelet coefficients of the noise-free images. The coefficients that correspond to the noise are assumed to approximate a Gaussian distribution. These models are then exploited to develop a Bayesian minimum mean squared error estimator. A method is presented for estimating the parameters of the assumed normal inverse Gaussian prior. Experiments are carried out on typical noise-free images corrupted with simulated white Gaussian noise. The results show that the proposed method performs better than some of the existing methods in terms of the peak signal-to-noise ratio and visual quality.