  Demosaicking and denoising are the first steps of any camera image processing
pipeline and are key for obtaining high quality RGB images. A promising current
research trend aims at solving these two problems jointly using convolutional
neural networks. Due to the unavailability of ground truth data these networks
cannot be currently trained using real RAW images. Instead, they resort to
simulated data. In this paper we present a method to learn demosaicking
directly from mosaicked images, without requiring ground truth RGB data. We
apply this to learn joint demosaicking and denoising only from RAW images, thus
enabling the use of real data. In addition we show that for this application
fine-tuning a network to a specific burst improves the quality of restoration
for both demosaicking and denoising.
