Abstract
The existing snow/rain removal methods often fail for
heavy snow/rain and dynamic scene. One reason for the
failure is due to the assumption that all the snowflakes/rain
streaks are sparse in snow/rain scenes. The other is that
the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video
desnowing and deraining to solve the problems mentioned
above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background
fluctuations and optical flow information, the detection of
moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs).
As for dense snowflakes/rain streaks, they are considered to
obey Gaussian distribution. The snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds
are removed by low-rank representation of the backgrounds.
Meanwhile, a group sparsity term in our model is designed
to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.