Abstract
An adaptive Bayesian estimator for image denoising in shearlet domain is presented, where the normal inverse Gaussian (NIG) distribution is used as the prior model of shearlet coefficients of images. The normal inverse Gaussian distribution can model a wide range of processes, from heavy-tailed to less heavy-tailed processes. Under this prior, a Bayesian shearlet estimator is derived by using the maximum a posteriori rule. Finally, a simulation is carried out to show the effectiveness of the new estimator. Experimental results show that the new estimator achieves state-of-art performance in terms of peak signal-to-noise ratio (PSNR) and visual quality.
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