  This presentation addresses the problem of reconstructing a high-resolution
image from multiple lower-resolution snapshots captured from slightly different
viewpoints in space and time. Key challenges for solving this problem include
(i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw
(noisy) images for maximal faithfulness to native camera data, and (iii)
designing/learning an image prior (regularizer) well suited to the task. We
address these three challenges with a hybrid algorithm building on the insight
from Wronski et al. that aliasing is an ally in this setting, with parameters
that can be learned end to end, while retaining the interpretability of
classical approaches to inverse problems. The effectiveness of our approach is
demonstrated on synthetic and real image bursts, setting a new state of the art
on several benchmarks and delivering excellent qualitative results on real raw
bursts captured by smartphones and prosumer cameras.
