DSLR cameras can achieve multiple zoom levels via shifting lens distances or
swapping lens types. However, these techniques are not possible on smartphone
devices due to space constraints. Most smartphone manufacturers adopt a hybrid
zoom system: commonly a Wide (W) camera at a low zoom level and a Telephoto (T)
camera at a high zoom level. To simulate zoom levels between W and T, these
systems crop and digitally upsample images from W, leading to significant
detail loss. In this paper, we propose an efficient system for hybrid zoom
super-resolution on mobile devices, which captures a synchronous pair of W and
T shots and leverages machine learning models to align and transfer details
from T to W. We further develop an adaptive blending method that accounts for
depth-of-field mismatches, scene occlusion, flow uncertainty, and alignment
errors. To minimize the domain gap, we design a dual-phone camera rig to
capture real-world inputs and ground-truths for supervised training. Our method
generates a 12-megapixel image in 500ms on a mobile platform and compares
favorably against state-of-the-art methods under extensive evaluation on
real-world scenarios.

提出了一种在移动设备上的混合变焦超分辨率的有效系统，通过捕捉一对同步的广角镜头和望远镜头拍摄的图像，并利用机器学习模型将细节从望远镜头转移到广角镜头，进一步提出了自适应混合方法以解决景深不匹配、场景遮挡、流动不确定性和对齐误差问题，并通过在真实情景下进行广泛评估，与最先进的方法进行比较，以在移动平台上 500ms 内生成一张 1200 万像素的图像。