  Photographs captured by smartphones and mid-range cameras have limited
spatial resolution and dynamic range, with noisy response in underexposed
regions and color artefacts in saturated areas. This paper introduces the first
approach (to the best of our knowledge) to the reconstruction of
high-resolution, high-dynamic range color images from raw photographic bursts
captured by a handheld camera with exposure bracketing. This method uses a
physically-accurate model of image formation to combine an iterative
optimization algorithm for solving the corresponding inverse problem with a
learned image representation for robust alignment and a learned natural image
prior. The proposed algorithm is fast, with low memory requirements compared to
state-of-the-art learning-based approaches to image restoration, and features
that are learned end to end from synthetic yet realistic data. Extensive
experiments demonstrate its excellent performance with super-resolution factors
of up to $\times 4$ on real photographs taken in the wild with hand-held
cameras, and high robustness to low-light conditions, noise, camera shake, and
moderate object motion.
