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.

通过使用真实的 RAW 传感器数据进行训练，本文表明将机器学习应用于数码变焦较为有利。作者们还展示了如何通过光学变焦图像获取高准确度的真实高分辨率图像用于训练深度神经网络，并提出了一种新的上下文双边损失（CoBi）算法，使得训练出的网络在 4 倍和 8 倍的计算缩放中实现了最佳性能。