  Motion blur is a frequently observed image artifact, especially under
insufficient illumination where exposure time has to be prolonged so as to
collect more photons for a bright enough image. Rather than simply removing
such blurring effects, recent researches have aimed at decomposing a blurry
image into multiple sharp images with spatial and temporal coherence. Since
motion blur decomposition itself is highly ambiguous, priors from neighbouring
frames or human annotation are usually needed for motion disambiguation. In
this paper, inspired by the complementary exposure characteristics of a global
shutter (GS) camera and a rolling shutter (RS) camera, we propose to utilize
the ordered scanline-wise delay in a rolling shutter image to robustify motion
decomposition of a single blurry image. To evaluate this novel dual imaging
setting, we construct a triaxial system to collect realistic data, as well as a
deep network architecture that explicitly addresses temporal and contextual
information through reciprocal branches for cross-shutter motion blur
decomposition. Experiment results have verified the effectiveness of our
proposed algorithm, as well as the validity of our dual imaging setting.
