High-quality images are crucial in remote sensing and UAV applications, but
atmospheric haze can severely degrade image quality, making image dehazing a
critical research area. Since the introduction of deep convolutional neural
networks, numerous approaches have been proposed, and even more have emerged
with the development of vision transformers and contrastive/few-shot learning.
Simultaneously, papers describing dehazing architectures applicable to various
Remote Sensing (RS) domains are also being published. This review goes beyond
the traditional focus on benchmarked haze datasets, as we also explore the
application of dehazing techniques to remote sensing and UAV datasets,
providing a comprehensive overview of both deep learning and prior-based
approaches in these domains. We identify key challenges, including the lack of
large-scale RS datasets and the need for more robust evaluation metrics, and
outline potential solutions and future research directions to address them.
This review is the first, to our knowledge, to provide comprehensive
discussions on both existing and very recent dehazing approaches (as of 2024)
on benchmarked and RS datasets, including UAV-based imagery.

在遥感和无人机应用中，高质量图像是至关重要的，但大气雾霾严重影响图像质量，因此图像去雾成为一个关键的研究领域。本综述超越了传统对基准雾霾数据集的关注点，还探讨了去雾技术在遥感和无人机数据集中的应用，全面概述了这些领域中的深度学习和基于先验的方法。我们确定了关键挑战，包括缺乏大规模遥感数据集和需要更加稳健的评估指标，并概述了解决这些挑战的潜在解决方案和未来研究方向。这是我们所知的第一篇详细讨论基准和遥感数据集上现有以及最近（截至 2024 年）的图像去雾方法的综述，包括基于无人机图像的内容。

去雾遥感与无人机影像：深度学习、基于先验和混合方法的综述

Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning,  Prior-based, and Hybrid Approaches

This article presents a comprehensive review of and analysis of
state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle)
applications, focusing on canopy-level and high-speed scenarios. This article
presents a comprehensive exploration of sensor technologies suitable for UAV
mapping, assessing their capabilities to provide measurements that meet the
requirements of fast UAV mapping. Furthermore, the study conducts extensive
experiments in a simulated environment to evaluate the performance of three
distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO),
and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping
speed, providing valuable insights into the strengths and limitations of each
algorithm. The results highlight the versatility and shortcomings of these
algorithms in meeting the demands of modern UAV applications. The findings
contribute to a nuanced understanding of UAV mapping dynamics, emphasizing
their applicability in complex environments and high-speed scenarios. This
research not only serves as a benchmark for mapping algorithm comparisons but
also offers practical guidance for selecting sensors tailored to specific UAV
mapping applications.

对无人机应用中基于林冠和高速场景的最新地图算法进行全面评估和分析，以及对适用于无人机地图绘制的传感器技术进行全面探索和评估，通过模拟环境进行广泛实验来评估三种不同的地图算法，即直接稀疏视觉里程计（DSO），双目 DSO（SDSO）和轻量级 DSO（DSOL）的性能，并突出了这些算法的优劣势以及它们在现代无人机应用中的适用性。研究结果旨在为地图算法比较提供基准，并为选择针对特定无人机地图绘制应用定制传感器提供实际指导。