  Denoising and demosaicking are essential yet correlated steps to reconstruct
a full color image from the raw color filter array (CFA) data. By learning a
deep convolutional neural network (CNN), significant progress has been achieved
to perform denoising and demosaicking jointly. However, most existing CNN-based
joint denoising and demosaicking (JDD) methods work on a single image while
assuming additive white Gaussian noise, which limits their performance on
real-world applications. In this work, we study the JDD problem for real-world
burst images, namely JDD-B. Considering the fact that the green channel has
twice the sampling rate and better quality than the red and blue channels in
CFA raw data, we propose to use this green channel prior (GCP) to build a
GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green
channels are utilized to guide the feature extraction and feature upsampling of
the whole image. To compensate for the shift between frames, the offset is also
estimated from GCP features to reduce the impact of noise. Our GCP-Net can
preserve more image structures and details than other JDD methods while
removing noise. Experiments on synthetic and real-world noisy images
demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.
