  Demosaicking and denoising are among the most crucial steps of modern digital
camera pipelines and their joint treatment is a highly ill-posed inverse
problem where at-least two-thirds of the information are missing and the rest
are corrupted by noise. This poses a great challenge in obtaining meaningful
reconstructions and a special care for the efficient treatment of the problem
is required. While there are several machine learning approaches that have been
recently introduced to deal with joint image demosaicking-denoising, in this
work we propose a novel deep learning architecture which is inspired by
powerful classical image regularization methods and large-scale convex
optimization techniques. Consequently, our derived network is more transparent
and has a clear interpretation compared to alternative competitive deep
learning approaches. Our extensive experiments demonstrate that our network
outperforms any previous approaches on both noisy and noise-free data. This
improvement in reconstruction quality is attributed to the principled way we
design our network architecture, which also requires fewer trainable parameters
than the current state-of-the-art deep network solution. Finally, we show that
our network has the ability to generalize well even when it is trained on small
datasets, while keeping the overall number of trainable parameters low.
