  Image demosaicking and denoising are the two key fundamental steps in digital
camera pipelines, aiming to reconstruct clean color images from noisy luminance
readings. In this paper, we propose and study Wild-JDD, a novel learning
framework for joint demosaicking and denoising in the wild. In contrast to
previous works which generally assume the ground truth of training data is a
perfect reflection of the reality, we consider here the more common imperfect
case of ground truth uncertainty in the wild. We first illustrate its
manifestation as various kinds of artifacts including zipper effect, color
moire and residual noise. Then we formulate a two-stage data degradation
process to capture such ground truth uncertainty, where a conjugate prior
distribution is imposed upon a base distribution. After that, we derive an
evidence lower bound (ELBO) loss to train a neural network that approximates
the parameters of the conjugate prior distribution conditioned on the degraded
input. Finally, to further enhance the performance for out-of-distribution
input, we design a simple but effective fine-tuning strategy by taking the
input as a weakly informative prior. Taking into account ground truth
uncertainty, Wild-JDD enjoys good interpretability during optimization.
Extensive experiments validate that it outperforms state-of-the-art schemes on
joint demosaicking and denoising tasks on both synthetic and realistic raw
datasets.
