In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …

The existing remote sensing (RS) image dehazing methods based on deep learning have
sought help from the convolutional frameworks. Nevertheless, the inherent limitations of …

Haze in hyperspectral images (HSIs) can lead to crosstalk between multiple bands, resulting
in errors that can be amplified and transmitted during data processing. As a consequence …

Recovering a clear image from a single hazy image is an open inverse problem. Although
significant research progress has been made most existing methods ignore the effect that …

Haze contamination severely degrades the quality and accuracy of optical remote sensing
(RS) images, including hyperspectral images (HSIs). Currently, there are no paired …

Abstract Vision Transformer (ViT) self-attention mechanism is characterized by feature
collapse in deeper layers, resulting in the vanishing of low-level visual features. However …

Remote sensing haze removal is a popular computational imaging technique that directly
obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based …

Remote sensing images are significantly degraded by bad weather conditions, such as haze
and sandstorms, which provide unhelpful support for valuable information extraction. Most …

Existing Transformer-based dehazing methods for remote sensing (RS) images, to avoid
quadratic computation complexity with respect to the feature map size, either perform self …

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