Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece.
Sensors (Basel). 2022 Jun 22;22(13):4707. doi: 10.3390/s22134707.
This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration methods serving the purpose of vision augmentation under adverse weather conditions. These image restoration methods are: (a) deraining; (b) desnowing; (c) dehazing ones. The contribution of this article is a survey of the recent literature on these three problem families, focusing on the utilization of deep learning (DL) models and meeting the requirements of their application in rescue operations. A faceted taxonomy is introduced in past and recent literature including various DL architectures, loss functions and datasets. Although there are multiple surveys on recovering images degraded by natural phenomena, the literature lacks a comprehensive survey focused explicitly on assisting FRs. This paper aims to fill this gap by presenting existing methods in the literature, assessing their suitability for FR applications, and providing insights for future research directions.
这篇综述文章关注的是视觉增强人工智能工具的出现，这些工具用于增强急救人员（FRs）在救援行动中的态势感知能力。更具体地说，本文调查了三种用于在恶劣天气条件下进行视觉增强的图像恢复方法：（a）去雨；（b）去雪；（c）去雾。本文的贡献在于对这三种问题方法的最新文献进行了调查，重点是利用深度学习（DL）模型并满足其在救援行动中的应用要求。引入了一个多维分类法，包括各种 DL 架构、损失函数和数据集，在过去和最近的文献中都有涉及。尽管有多个关于恢复受自然现象影响的图像的调查，但文献中缺乏明确针对帮助 FRs 的综合调查。本文旨在通过呈现文献中的现有方法，评估它们对 FR 应用的适用性，并为未来的研究方向提供见解，来填补这一空白。
