  It is a challenging task to remove heavy and mixed types of noise from
Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex
approach to RPCA for HSI denoising, which adopts the log-determinant rank
approximation and a novel $\ell_{2,\log}$ norm, to restrict the low-rank or
column-wise sparse properties for the component matrices, respectively.For the
$\ell_{2,\log}$-regularized shrinkage problem, we develop an efficient,
closed-form solution, which is named $\ell_{2,\log}$-shrinkage operator, which
can be generally used in other problems. Extensive experiments on both
simulated and real HSIs demonstrate the effectiveness of the proposed method in
denoising HSIs.
