We present a novel deep learning framework that models the scene dependent
image processing inside cameras. Often called as the radiometric calibration,
the process of recovering RAW images from processed images (JPEG format in the
sRGB color space) is essential for many computer vision tasks that rely on
physically accurate radiance values. All previous works rely on the
deterministic imaging model where the color transformation stays the same
regardless of the scene and thus they can only be applied for images taken
under the manual mode. In this paper, we propose a data-driven approach to
learn the scene dependent and locally varying image processing inside cameras
under the automode. Our method incorporates both the global and the local scene
context into pixel-wise features via multi-scale pyramid of learnable histogram
layers. The results show that we can model the imaging pipeline of different
cameras that operate under the automode accurately in both directions (from RAW
to sRGB, from sRGB to RAW) and we show how we can apply our method to improve
the performance of image deblurring.

本文提出了一种新颖的深度学习框架，用于对场景相关的相机内图像处理进行建模，通过多尺度金字塔学习直方图层将全局和局部场景上下文结合到像素特征中，该方法可以精确地建模在自动模式下不同相机的成像管道，并应用于改善图像去模糊的性能。

使用深度神经网络对相机中场景相关成像建模

Modelling the Scene Dependent Imaging in Cameras with a Deep Neural  Network

The OSIRIS-REx Thermal Emission Spectrometer (OTES) will provide remote
measurements of mineralogy and thermophysical properties of Bennu to map its
surface, help select the OSIRIS-REx sampling site, and investigate the
Yarkovsky effect. OTES is a Fourier transform spectrometer covering the
spectral range 5.71 - 100 {\mu}m (1750 - 100 cm-1) with a spectral sample
interval of 8.66 cm-1 and a 6.5-mrad field of view. The OTES telescope is a
15.2-cm diameter Cassegrain telescope that feeds a flat-plate Michelson moving
mirror mounted on a linear voice-coil motor assembly. A single uncooled
deuterated L-alanine doped triglycine sulfate (DLATGS) pyroelectric detector is
used to sample the interferogram every two seconds. Redundant ~0.855 {\mu}m
laser diodes are used in a metrology interferometer to provide precise moving
mirror control and IR sampling at 772 Hz. The beamsplitter is a 38-mm diameter,
1-mm thick chemical vapor deposited diamond with an antireflection
microstructure to minimize surface reflection. An internal calibration cone
blackbody target provides radiometric calibration. The radiometric precision in
a single spectrum is <= 2.2 x 10-8 W cm-2 sr-1/cm-1 between 300 and 1350 cm-1.
The absolute integrated radiance error is <1% for scene temperatures ranging
from 150 to 380 K. The overall OTES envelope size is 37.5 x 28.9 x 52.2 cm, and
the mass is 6.27 kg. The power consumption is 10.8 W average. The OTES was
developed by Arizona State University with Moog Broad Reach developing the
electronics. OTES was integrated, tested, and radiometrically calibrated on the
Arizona State University campus in Tempe, AZ.

该研究介绍了 OSIRIS-REx Thermal Emission Spectrometer 的设计和特点，以及它在探索 Bennu 表面矿物学、热物理学特性和研究雅可夫斯基效应方面的应用。

OSIRIS-REx 热辐射光谱仪（OTES）仪器

The OSIRIS-REx Thermal Emission Spectrometer (OTES) Instrument

We present a novel method for communicating between a camera and display by
embedding and recovering hidden and dynamic information within a displayed
image. A handheld camera pointed at the display can receive not only the
display image, but also the underlying message. These active scenes are
fundamentally different from traditional passive scenes like QR codes because
image formation is based on display emittance, not surface reflectance.
Detecting and decoding the message requires careful photometric modeling for
computational message recovery. Unlike standard watermarking and steganography
methods that lie outside the domain of computer vision, our message recovery
algorithm uses illumination to optically communicate hidden messages in real
world scenes. The key innovation of our approach is an algorithm that performs
simultaneous radiometric calibration and message recovery in one convex
optimization problem. By modeling the photometry of the system using a
camera-display transfer function (CDTF), we derive a physics-based kernel
function for support vector machine classification. We demonstrate that our
method of optimal online radiometric calibration (OORC) leads to an efficient
and robust algorithm for computational messaging between nine commercial
cameras and displays.

该研究介绍了一种新方法，通过在显示图像中嵌入和恢复隐藏和动态信息来在相机和显示器之间进行通信，并使用光学通信实现隐藏信息的计算机化消息传递。

摄像机 - 显示器通信的最佳辐射校准

Optimal Radiometric Calibration for Camera-Display Communication

To produce images that are suitable for display, tone-mapping is widely used
in digital cameras to map linear color measurements into narrow gamuts with
limited dynamic range. This introduces non-linear distortion that must be
undone, through a radiometric calibration process, before computer vision
systems can analyze such photographs radiometrically. This paper considers the
inherent uncertainty of undoing the effects of tone-mapping. We observe that
this uncertainty varies substantially across color space, making some pixels
more reliable than others. We introduce a model for this uncertainty and a
method for fitting it to a given camera or imaging pipeline. Once fit, the
model provides for each pixel in a tone-mapped digital photograph a probability
distribution over linear scene colors that could have induced it. We
demonstrate how these distributions can be useful for visual inference by
incorporating them into estimation algorithms for a representative set of
vision tasks.

本文研究了色调映射的效应对于数字照片的非线性失真以及该失真造成的校准不确定性。我们提出并实现了一种模型和方法来解决这个问题，并用不同的视觉任务来展示了这种方法的有效性。