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

Different from the ground image with uniform haze, the haze in remote sensing (RS) image
has the characteristics of irregular shape and uneven concentration in hazy weather. It …

Remote sensing (RS) image dehazing is an effective means to enhance the quality of hazy
RS images. However, existing dehazing methods are ineffective in dealing with …

Recently, U-shaped neural networks have gained widespread application in remote sensing
image dehazing and achieved promising performance. However, most of the existing U …

Space probes are always obstructed by floating objects in the atmosphere (clouds, haze,
rain, etc.) during imaging, resulting in the loss of a significant amount of detailed information …

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

Remote sensing (RS) image dehazing holds immense importance for enhancing the utility of
remote sensing technology across both military and civilian domains. Due to the ineffective …

Image dehazing is an important preprocessing task since haze extremely degrades the
image quality and hampers the application of remote sensing vision system. Although the …

Haze or cloud always shrouds satellite images, obscuring valuable geographic information
for military surveillance, natural calamity surveillance and mineral resource exploration …

Recently, U-Net architecture has been extensively explored for remote sensing (RS) image
haze removal, demonstrating remarkable performance. However, most existing RS image …