  This paper shows that when applying machine learning to digital zoom for
photography, it is beneficial to use real, RAW sensor data for training.
Existing learning-based super-resolution methods do not use real sensor data,
instead operating on RGB images. In practice, these approaches result in loss
of detail and accuracy in their digitally zoomed output when zooming in on
distant image regions. We also show that synthesizing sensor data by resampling
high-resolution RGB images is an oversimplified approximation of real sensor
data and noise, resulting in worse image quality. The key barrier to using real
sensor data for training is that ground truth high-resolution imagery is
missing. We show how to obtain the ground-truth data with optically zoomed
images and contribute a dataset, SR-RAW, for real-world computational zoom. We
use SR-RAW to train a deep network with a novel contextual bilateral loss
(CoBi) that delivers critical robustness to mild misalignment in input-output
image pairs. The trained network achieves state-of-the-art performance in 4X
and 8X computational zoom.
