Remote sensing (RS) dehazing is a long-standing topic, as the contrast and clarity of images
are seriously reduced in the severe environment. Recent learning-based ways achieve …

Abstract Information about the earth's surface is difficult to capture in remote sensing images
because bad weather greatly curtails visibility and diminishes visual contrast in the images …

Haze, generated by floaters (semitransparent clouds, fog, snow, etc.) in the atmosphere, can
significantly degrade the utilization of remote sensing images (RSIs). However, the existing …

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

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) dehazing is a challenge topic, as images captured under hazy
scenarios often suffer from seriously quality degradation and inconsistency. RS image …

Image dehazing is crucial for improving the advanced applications on remote sensing (RS)
images. However, collecting paired RS images to train the deep neural networks (DNNs) is …

The degradation of visual quality in remote sensing images caused by haze presents
significant challenges in interpreting and extracting essential information. To effectively …

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 …