Deep Image Demosaicking using a Cascade of
Convolutional Residual Denoising Networks
Abstract. 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.