  Stereo images, containing left and right view images with disparity, are
utilized in solving low-vision tasks recently, e.g., rain removal and
super-resolution. Stereo image restoration methods usually obtain better
performance than monocular methods by learning the disparity between dual views
either implicitly or explicitly. However, existing stereo rain removal methods
still cannot make full use of the complementary information between two views,
and we find it is because: 1) the rain streaks have more complex distributions
in directions and densities, which severely damage the complementary
information and pose greater challenges; 2) the disparity estimation is not
accurate enough due to the imperfect fusion mechanism for the features between
two views. To overcome such limitations, we propose a new \underline{Stereo}
\underline{I}mage \underline{R}ain \underline{R}emoval method (StereoIRR) via
sufficient interaction between two views, which incorporates: 1) a new
Dual-view Mutual Attention (DMA) mechanism which generates mutual attention
maps by taking left and right views as key information for each other to
facilitate cross-view feature fusion; 2) a long-range and cross-view
interaction, which is constructed with basic blocks and dual-view mutual
attention, can alleviate the adverse effect of rain on complementary
information to help the features of stereo images to get long-range and
cross-view interaction and fusion. Notably, StereoIRR outperforms other related
monocular and stereo image rain removal methods on several datasets. Our codes
and datasets will be released.
