  This letter proposes a simple method of transferring rain structures of a
given exemplar rain image into a target image. Given the exemplar rain image
and its corresponding masked rain image, rain patches including rain structures
are extracted randomly, and then residual rain patches are obtained by
subtracting those rain patches from their mean patches. Next, residual rain
patches are selected randomly, and then added to the given target image along a
raster scanning direction. To decrease boundary artifacts around the added
patches on the target image, minimum error boundary cuts are found using
dynamic programming, and then blending is conducted between overlapping
patches. Our experiment shows that the proposed method can generate realistic
rain images that have similar rain structures in the exemplar images. Moreover,
it is expected that the proposed method can be used for rain removal. More
specifically, natural images and synthetic rain images generated via the
proposed method can be used to learn classifiers, for example, deep neural
networks, in a supervised manner.
