  Rain streaks and rain drops are two natural phenomena, which degrade image
capture in different ways. Currently, most existing deep deraining networks
take them as two distinct problems and individually address one, and thus
cannot deal adequately with both simultaneously. To address this, we propose a
Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing
both rain streaks and raindrops. Inside the DAM, there are two attentive maps -
each of which attends to the heavy and light rainy regions, respectively, to
guide the deraining process differently for applicable regions. In addition, to
further refine the result, a Differential-driven Dual Attention-in-Attention
Model (D-DAiAM) is proposed with a "heavy-to-light" scheme to remove rain via
addressing the unsatisfying deraining regions. Extensive experiments on one
public raindrop dataset, one public rain streak and our synthesized joint rain
streak and raindrop (JRSRD) dataset have demonstrated that the proposed method
not only is capable of removing rain streaks and raindrops simultaneously, but
also achieves the state-of-the-art performance on both tasks.
