We address the problem of non-blind deblurring and demosaicking of noisy raw images. We adapt an existing learningbased 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 colordependent point-spread function) from real images, in essence deblurring sharp images.