  Denoising images contaminated by the mixture of additive white Gaussian noise
(AWGN) and impulse noise (IN) is an essential but challenging problem. The
presence of impulsive disturbances inevitably affects the distribution of
noises and thus largely degrades the performance of traditional AWGN denoisers.
Existing methods target to compensate the effects of IN by introducing a
weighting matrix, which, however, is lack of proper priori and thus hard to be
accurately estimated. To address this problem, we exploit the Pareto
distribution as the priori of the weighting matrix, based on which an accurate
and robust weight estimator is proposed for mixed noise removal. Particularly,
a relatively small portion of pixels are assumed to be contaminated with IN,
which should have weights with small values and then be penalized out. This
phenomenon can be properly described by the Pareto distribution of type 1.
Therefore, armed with the Pareto distribution, we formulate the problem of
mixed noise removal in the Bayesian framework, where nonlocal self-similarity
priori is further exploited by adopting nonlocal low rank approximation.
Compared to existing methods, the proposed method can estimate the weighting
matrix adaptively, accurately, and robust for different level of noises, thus
can boost the denoising performance. Experimental results on widely used image
datasets demonstrate the superiority of our proposed method to the
state-of-the-arts.
