  This article addresses the image denoising problem in the situations of
strong noise. We propose a dual sparse decomposition method. This method makes
a sub-dictionary decomposition on the over-complete dictionary in the sparse
decomposition. The sub-dictionary decomposition makes use of a novel criterion
based on the occurrence frequency of atoms of the over-complete dictionary over
the data set. The experimental results demonstrate that the
dual-sparse-decomposition method surpasses state-of-art denoising performance
in terms of both peak-signal-to-noise ratio and
structural-similarity-index-metric, and also at subjective visual quality.
