  We address the problem of non-blind deblurring and demosaicking of noisy raw
images. We adapt an existing learning-based approach to RGB image deblurring to
handle raw images by introducing a new interpretable module that jointly
demosaicks and deblurs them. We train this model on RGB images converted into
raw ones following a realistic invertible camera pipeline. We demonstrate the
effectiveness of this model over two-stage approaches stacking demosaicking and
deblurring modules on quantitive benchmarks. We also apply our approach to
remove a camera's inherent blur (its color-dependent point-spread function)
from real images, in essence deblurring sharp images.
