  The motivation of this paper is to introduce a novel framework for the
restoration of images corrupted by mixed Gaussian-impulse noise. To this aim,
first, an adaptive curvelet thresholding criterion is proposed which tries to
adaptively remove the perturbations appeared during denoising process. Then, a
new statistical regularization term, called joint adaptive statistical prior
(JASP), is established which enforces both the local and nonlocal statistical
consistencies, simultaneously, in a unified manner. Furthermore, a novel
technique for mixed Gaussian plus impulse noise removal using JASP in a
variational scheme is developed--we refer to it as De-JASP. To efficiently
solve the above variational scheme, an efficient alternating minimization
algorithm based on split Bregman iterative framework is developed. Extensive
experimental results manifest the effectiveness of the proposed method
comparing with the current state-of-the-art methods in mixed Gaussian-impulse
noise removal.
