This article aims to identify core research directions and provide a comprehensive overview of major advancements in the field of hypercomplex signal and image processing techniques using network graph theory. The methodology employs community detection algorithms on research networks to uncover relationships among researchers and topic fields in the hypercomplex domain. This is accomplished through a comprehensive academic database search and metadata analysis from pertinent papers. The article focuses on the utility of these techniques in various applications and the value of mathematically rich frameworks. The results demonstrate how optimized network-based approaches can determine common topics and emerging lines of research. The article identifies distinct core research directions, including significant advancements in image/video processing, computer vision, signal processing, security, navigation, and machine learning within the hypercomplex domain. Current trends, challenges, opportunities, and the most promising directions in hypercomplex signal and image processing are highlighted based on a thorough literature analysis. This provides actionable insights for researchers to advance this domain.

中文翻译：

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使用网络图论的信号和图像处理中的超复杂技术：确定核心研究方向[超复杂信号和图像处理]


本文旨在确定核心研究方向，并全面概述使用网络图理论的超复杂信号和图像处理技术领域的主要进展。该方法在研究网络上采用社区检测算法来揭示超复杂领域中研究人员和主题领域之间的关系。这是通过全面的学术数据库搜索和相关论文的元数据分析来完成的。本文重点介绍这些技术在各种应用中的实用性以及丰富的数学框架的价值。结果证明了基于网络的优化方法如何确定共同主题和新兴研究领域。该文章确定了不同的核心研究方向，包括超复杂领域内图像/视频处理、计算机视觉、信号处理、安全、导航和机器学习方面的重大进展。基于全面的文献分析，重点介绍了超复杂信号和图像处理的当前趋势、挑战、机遇和最有前途的方向。这为研究人员推进该领域的发展提供了可行的见解。