Haze superimposes a veil over remote sensing images, which severely limits the extraction
of valuable military information. To this end, we present a novel trinity model to restore …

Recently, the U-shaped networks have been widely explored in remote sensing image
dehazing and obtained promising performance. However, most of the existing dehazing …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

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

Existing dehazing algorithms are not effective for remote sensing images (RSIs) with dense
haze, and dehazed results are prone to over-enhancement, color distortion, and artifacts. To …

Remote sensing image dehazing is of great scientific interest and application value in both
military and civil fields. In this article, we propose an enhanced attention-guide generative …

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

In many remote sensing (RS) applications, haze seriously degrades the quality of optical RS
images and even brings inconvenience to the following high-level visual tasks such as RS …

Multispectral remote sensing (RS) images are often contaminated by the haze that degrades
the quality of RS data and reduces the accuracy of interpretation and classification …

Haze seriously reduces the quality of optical remote sensing images, resulting in poor
performance in many applications, such as remote sensing image change detection and …