  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.
