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。

远距学习，学以致远

Zoom To Learn, Learn To Zoom

Existing methods for single image super-resolution (SR) are typically
evaluated with synthetic degradation models such as bicubic or Gaussian
downsampling. In this paper, we investigate SR from the perspective of camera
lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff
between resolution (R) and field-of-view (V) in realistic imaging systems.
Specifically, we view the R-V degradation as a latent model in the SR process
and learn to reverse it with realistic low- and high-resolution image pairs. To
obtain the paired images, we propose two novel data acquisition strategies for
two representative imaging systems (i.e., DSLR and smartphone cameras),
respectively. Based on the obtained City100 dataset, we quantitatively analyze
the performance of commonly-used synthetic degradation models, and demonstrate
the superiority of CameraSR as a practical solution to boost the performance of
existing SR methods. Moreover, CameraSR can be readily generalized to different
content and devices, which serves as an advanced digital zoom tool in realistic
imaging systems. Codes and datasets are available at
this https URL

研究了基于摄像头镜头的 CameraSR 方法，旨在改善实际成像系统中分辨率（R）和视野范围（V）之间的固有平衡问题，并提出了两种新的数据采集策略， City100 数据集表明， CameraSR 具有提高单图超分辨率性能的实际解决方案，并且可以快速推广到不同的内容和设备上作为实现数字变焦的高级工具。