Deep learning based methods have achieved remarkable success in image
restoration and enhancement, but most such methods rely on RGB input images.
These methods fail to take into account the rich spectral distribution of
natural images. We propose a deep architecture, SpecNet, which computes
spectral profile to estimate pixel-wise dynamic range adjustment of a given
image. First, we employ an unpaired cycle-consistent framework to generate
hyperspectral images (HSI) from low-light input images. HSI is further used to
generate a normal light image of the same scene. We incorporate a
self-supervision and a spectral profile regularization network to infer a
plausible HSI from an RGB image. We evaluate the benefits of optimizing the
spectral profile for real and fake images in low-light conditions on the LOL
Dataset.

通过深度学习，提出了一种 SpecNet 深度架构，该架构计算光谱剖面以估计给定图像的像素动态范围调整，进而实现在低光条件下用环境一致框架生成高光谱图像，并通过自我监督和光谱剖面规则化网络来从 RGB 图像推断出可能的高光谱图像。

通过高光谱域映射学习提高视觉质量

Learning to Enhance Visual Quality via Hyperspectral Domain Mapping

Finding correspondences between shapes is a fundamental problem in computer
vision and graphics, which is relevant for many applications, including 3D
reconstruction, object tracking, and style transfer. The vast majority of
correspondence methods aim to find a solution between pairs of shapes, even if
multiple instances of the same class are available. While isometries are often
studied in shape correspondence problems, they have not been considered
explicitly in the multi-matching setting. This paper closes this gap by
proposing a novel optimisation formulation for isometric multi-shape matching.
We present a suitable optimisation algorithm for solving our formulation and
provide a convergence and complexity analysis. Our algorithm obtains
multi-matchings that are by construction provably cycle-consistent. We
demonstrate the superior performance of our method on various datasets and set
the new state-of-the-art in isometric multi-shape matching.

本文提出了新型的等距多形状匹配问题的优化公式，并介绍了适合解决这种公式的优化算法，提供了收敛性和复杂性分析，对不同数据集进行了实验，取得了最新的等距多形状匹配的最好表现。

等距多形状匹配

Isometric Multi-Shape Matching

In this work we study permutation synchronisation for the challenging case of
partial permutations, which plays an important role for the problem of matching
multiple objects (e.g. images or shapes). The term synchronisation refers to
the property that the set of pairwise matchings is cycle-consistent, i.e. in
the full matching case all compositions of pairwise matchings over cycles must
be equal to the identity. Motivated by clustering and matrix factorisation
perspectives of cycle-consistency, we derive an algorithm to tackle the
permutation synchronisation problem based on non-negative factorisations. In
order to deal with the inherent non-convexity of the permutation
synchronisation problem, we use an initialisation procedure based on a novel
rotation scheme applied to the solution of the spectral relaxation. Moreover,
this rotation scheme facilitates a convenient Euclidean projection to obtain a
binary solution after solving our relaxed problem. In contrast to
state-of-the-art methods, our approach is guaranteed to produce
cycle-consistent results. We experimentally demonstrate the efficacy of our
method and show that it achieves better results compared to existing methods.

针对频繁出现在匹配多个对象（例如图像或形状）问题中的部分排列，我们研究置换同步问题。使用基于非负因式分解的算法解决了置换同步问题，采用一种新的旋转方案进行初始化，以方便欧几里得投影，得到了二进制解。与现有的方法相比，我们的方法保证产生一致的结果，并在实验中证明了其有效性。