Whether to attract viewer attention to a particular object, give the
impression of depth or simply reproduce human-like scene perception, shallow
depth of field images are used extensively by professional and amateur
photographers alike. To this end, high quality optical systems are used in DSLR
cameras to focus on a specific depth plane while producing visually pleasing
bokeh. We propose a physically motivated pipeline to mimic this effect from
all-in-focus stereo images, typically retrieved by mobile cameras. It is
capable to change the focal plane a posteriori at 76 FPS on KITTI images to
enable real-time applications. As our portmanteau suggests, SteReFo
interrelates stereo-based depth estimation and refocusing efficiently. In
contrast to other approaches, our pipeline is simultaneously fully
differentiable, physically motivated, and agnostic to scene content. It also
enables computational video focus tracking for moving objects in addition to
refocusing of static images. We evaluate our approach on the publicly available
datasets SceneFlow, KITTI, CityScapes and quantify the quality of architectural
changes.

本文提出了一个基于双目图像深度估计和重聚的物理学启示管道，以在移动相机中从全聚焦双目图像中模仿 DSLR 相机的浅景深效果，从而实现在 76 FPS 的实时应用。相对于其他方法，我们的管道同时是可完全区分的、受物理启发的、不受场景内容的影响，还可以在静态图像场景中实现计算机视频焦点跟踪，我们还使用 SceneFlow、KITTI、CityScapes 等公共数据集对我们的方法进行了评估。

SteReFo: 基于立体视觉的高效图像重新聚焦

SteReFo: Efficient Image Refocusing with Stereo Vision

The modulation transfer function (MTF) is widely used to characterise the
performance of optical systems. Measuring it is costly and it is thus rarely
available for a given lens specimen. Instead, MTFs based on simulations or, at
best, MTFs measured on other specimens of the same lens are used. Fortunately,
images recorded through an optical system contain ample information about its
MTF, only that it is confounded with the statistics of the images. This work
presents a method to estimate the MTF of camera lens systems directly from
photographs, without the need for expensive equipment. We use a custom grid
display to accurately measure the point response of lenses to acquire ground
truth training data. We then use the same lenses to record natural images and
employ a data-driven supervised learning approach using a convolutional neural
network to estimate the MTF on small image patches, aggregating the information
into MTF charts over the entire field of view. It generalises to unseen lenses
and can be applied for single photographs, with the performance improving if
multiple photographs are available.

本文提出一种使用卷积神经网络从摄影中直接估算相机镜头系统的调制转移函数，无需昂贵设备的方法。

自动估算调制传递函数

Automatic Estimation of Modulation Transfer Functions

The number of parameters describing a quantum state is well known to grow
exponentially with the number of particles. This scaling clearly limits our
ability to do tomography to systems with no more than a few qubits and has been
used to argue against the universal validity of quantum mechanics itself.
However, from a computational learning theory perspective, it can be shown
that, in a probabilistic setting, quantum states can be approximately learned
using only a linear number of measurements. Here we experimentally demonstrate
this linear scaling in optical systems with up to 6 qubits. Our results
highlight the power of computational learning theory to investigate quantum
information, provide the first experimental demonstration that quantum states
can be "probably approximately learned" with access to a number of copies of
the state that scales linearly with the number of qubits, and pave the way to
probing quantum states at new, larger scales.

本研究着重于探讨 “计算学习理论” 在探究量子信息中的应用，发现在概率性的前提下，仅通过线性数量的测量即可近似学习量子态，实验结果在光学系统中展示了线性比例的特点，为量子状态探究开辟了新的大规模可能性。