Computational spectral imaging is drawing increasing attention owing to the
snapshot advantage, and amplitude, phase, and wavelength encoding systems are
three types of representative implementations. Fairly comparing and
understanding the performance of these systems is essential, but challenging
due to the heterogeneity in encoding design. To overcome this limitation, we
propose the unified encoding model (UEM) that covers all physical systems using
the three encoding types. Specifically, the UEM comprises physical amplitude,
physical phase, and physical wavelength encoding models that can be combined
with a digital decoding model in a joint encoder-decoder optimization framework
to compare the three systems under a unified experimental setup fairly.
Furthermore, we extend the UEMs to ideal versions, namely, ideal amplitude,
ideal phase, and ideal wavelength encoding models, which are free from physical
constraints, to explore the full potential of the three types of computational
spectral imaging systems. Finally, we conduct a holistic comparison of the
three types of computational spectral imaging systems and provide valuable
insights for designing and exploiting these systems in the future.

基于统一编码模型，本文比较并理解了物理幅度、物理相位和物理波长编码三种计算光谱成像系统的性能，并通过理想版本的编码模型探索了这三种系统的潜力，最终为未来设计和应用这些系统提供了有价值的见解。

计算光谱成像与统一编码模型：比较研究与未来

Computational Spectral Imaging with Unified Encoding Model: A  Comparative Study and Beyond

An important element of the $S$-matrix bootstrap program is the relationship
between the modulus of an $S$-matrix element and its phase. Unitarity relates
them by an integral equation. Even in the simplest case of elastic scattering,
this integral equation cannot be solved analytically and numerical approaches
are required. We apply modern machine learning techniques to studying the
unitarity constraint. We find that for a given modulus, when a phase exists it
can generally be reconstructed to good accuracy with machine learning.
Moreover, the loss of the reconstruction algorithm provides a good proxy for
whether a given modulus can be consistent with unitarity at all. In addition,
we study the question of whether multiple phases can be consistent with a
single modulus, finding novel phase-ambiguous solutions. In particular, we find
a new phase-ambiguous solution which pushes the known limit on such solutions
significantly beyond the previous bound.

应用现代机器学习技术研究了 $S$- 矩阵约束条件，发现在给定模数的情况下，可以通过机器学习方法准确地重建出相位，并且重建算法的失真度可以作为判断给定模数是否符合单位性的良好代理。此外，我们研究了单个模数是否可以与多个相位一致的问题，发现了新的相位模棱两可的解决方案，将已知解的限制显著推向了前所未有的界限。

用机器学习重构 $S$ 矩阵相位

Reconstructing $S$-matrix Phases with Machine Learning

This work advocates Eulerian motion representation learning over the current
standard Lagrangian optical flow model. Eulerian motion is well captured by
using phase, as obtained by decomposing the image through a complex-steerable
pyramid. We discuss the gain of Eulerian motion in a set of practical use
cases: (i) action recognition, (ii) motion prediction in static images, (iii)
motion transfer in static images and, (iv) motion transfer in video. For each
task we motivate the phase-based direction and provide a possible approach.

本文探讨了欧拉运动在四个实际应用中（动作识别、静态图像中的运动预测、静态图像中的运动转换以及视频中的运动转换）的优点，并通过对图像进行复杂可控金字塔分解获取相位来捕获欧拉运动，而不是当前标准的拉格朗日光流模型。