  Removing raindrops in images has been addressed as a significant task for
various computer vision applications. In this paper, we propose the first
method using a Dual-Pixel (DP) sensor to better address the raindrop removal.
Our key observation is that raindrops attached to a glass window yield
noticeable disparities in DP's left-half and right-half images, while almost no
disparity exists for in-focus backgrounds. Therefore, DP disparities can be
utilized for robust raindrop detection. The DP disparities also brings the
advantage that the occluded background regions by raindrops are shifted between
the left-half and the right-half images. Therefore, fusing the information from
the left-half and the right-half images can lead to more accurate background
texture recovery. Based on the above motivation, we propose a DP Raindrop
Removal Network (DPRRN) consisting of DP raindrop detection and DP fused
raindrop removal. To efficiently generate a large amount of training data, we
also propose a novel pipeline to add synthetic raindrops to real-world
background DP images. Experimental results on synthetic and real-world datasets
demonstrate that our DPRRN outperforms existing state-of-the-art methods,
especially showing better robustness to real-world situations. Our source code
and datasets are available at http://www.ok.sc.e.titech.ac.jp/res/SIR/.
