Cloud contamination is a common issue that severely reduces the quality of optical satellite
images in remote sensing fields. With the rapid development of deep learning technology …

Polarization imaging, which provides multidimensional information beyond traditional
intensity imaging, has prominent advantages for complex imaging tasks, particularly in …

Compared to pixel-level content loss, domain-level style loss in CycleGAN-based dehazing
algorithms just imposes relatively soft constraints on the intermediate translated images …

Remote sensing (RS) dehazing is a challenging task since various haze distributions
severely degrade the image quality. Recent learning-based methods achieve dramatic …

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 …

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 …

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 …