We investigate the applicability of quaternion-valued wavelets on the plane
to holistic colour image processing. We present a methodology for decomposing
and reconstructing colour images using quaternionic wavelet filters associated
to recently developed quaternion-valued wavelets on the plane. We consider
compression, enhancement, segmentation, and denoising techniques to demonstrate
quaternion-valued wavelets as a promising tool for holistic colour image
processing.

研究平面上的四元波纹在整体颜色图像处理中的适用性，并提出了一种使用平面上最近开发的四元波纹滤波器关联的四元波纹对颜色图像进行分解和重构的方法。我们考虑了压缩、增强、分割和降噪技术，以证明四元波纹是整体颜色图像处理的一种有希望的工具。

在平面上使用新颖的四元数值小波进行色彩图像的整体处理

Holistic Processing of Colour Images Using Novel Quaternion-Valued  Wavelets on the Plane

Ranking, recommendation, and retrieval systems are widely used in online
platforms and other societal systems, including e-commerce, media-streaming,
admissions, gig platforms, and hiring. In the recent past, a large "fair
ranking" research literature has been developed around making these systems
fair to the individuals, providers, or content that are being ranked. Most of
this literature defines fairness for a single instance of retrieval, or as a
simple additive notion for multiple instances of retrievals over time. This
work provides a critical overview of this literature, detailing the often
context-specific concerns that such an approach misses: the gap between high
ranking placements and true provider utility, spillovers and compounding
effects over time, induced strategic incentives, and the effect of statistical
uncertainty. We then provide a path forward for a more holistic and
impact-oriented fair ranking research agenda, including methodological lessons
from other fields and the role of the broader stakeholder community in
overcoming data bottlenecks and designing effective regulatory environments.

对在线平台中对个人、提供方或内容进行公正排序的 “公正排名” 研究文献进行了批判性概述，提出了更全面和以影响为导向的公正排名研究议程的前景，包括其他领域的方法论教训以及更广泛利益相关者社区在克服数据瓶颈和设计有效监管环境方面的作用。