Image dehazing is a challenging task in computer vision. Currently, most dehazing methods
adopt the U-Net architecture that directly fuses the decoding layer with the corresponding …

Images captured in hazy environments need to be processed to increase their contrast and
colour integrity. Dehazing, sometimes referred to as haze removal is an important pre …

Current methods for remote sensing image dehazing confront noteworthy computational
intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic …

Benefiting from the excellent texture recovery capability of generative adversarial networks
(GANs), generated images are capable of maintaining clear texture features even when …

Low-light remote sensing image enhancement for unmanned aerial vehicles (UAVs) has
significant scientific and practical value because unfavorable lighting conditions make …

Single-image dehazing plays a significant preprocessing role in machine vision tasks. As
the dark-channel-prior method will fail in the sky region of the image, resulting in …

Ship detection aims to automatically identify whether there are ships in the images, precisely
classifies and localizes them. Regardless of whether utilizing early manually designed …

Haze pollution in hyperspectral images (HSIs) leads to surface information lack and image
clarity degradation, which seriously affects the performance of subsequent image …

Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when
confronted with the intricacies of real-world hazy environments. While learning-based …

Various vision-based tasks suffer from inaccurate navigation and poor performance due to
inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and …