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。

移动电话上使用相机融合的高效混合变焦

Efficient Hybrid Zoom using Camera Fusion on Mobile Phones

The impressive growth of smartphone devices in combination with the rising
ubiquity of using mobile platforms for sensitive applications such as Internet
banking, have triggered a rapid increase in mobile malware. In recent
literature, many studies examine Machine Learning techniques, as the most
promising approach for mobile malware detection, without however quantifying
the uncertainty involved in their detections. In this paper, we address this
problem by proposing a machine learning dynamic analysis approach that provides
provably valid confidence guarantees in each malware detection. Moreover the
particular guarantees hold for both the malicious and benign classes
independently and are unaffected by any bias in the data. The proposed approach
is based on a novel machine learning framework, called Conformal Prediction,
combined with a random forests classifier. We examine its performance on a
large-scale dataset collected by installing 1866 malicious and 4816 benign
applications on a real android device. We make this collection of dynamic
analysis data available to the research community. The obtained experimental
results demonstrate the empirical validity, usefulness and unbiased nature of
the outputs produced by the proposed approach.

通过结合智能手机设备的显著增长以及在敏感应用程序（如互联网银行）中使用移动平台的快速普及，使移动恶意软件迅速增加。本文提出了一种机器学习动态分析方法，为每个恶意软件检测提供可证明有效的置信保证，并且该保证对恶意和良性类别都适用并不受数据偏差的影响。所提出的方法基于一种名为一致性预测的新型机器学习框架，结合了随机森林分类器，在一个真实的 Android 设备上安装了 1866 个恶意应用程序和 4816 个良性应用程序的大规模数据集进行了性能测试。实验结果证明了所提出方法产生的输出的经验有效性、有用性和无偏性。