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

As a newly emerging advance in deep generative models, diffusion models have achieved
state-of-the-art results in many fields, including computer vision, natural language …

Originally designed for medical segmentation, the U-Net model excels in ocean remote
sensing for segmentation, forecasting, and image enhancement. We propose …

Due to hardware technology limitations, satellite sensors are unable to capture images with
high temporal, spatial, and spectral resolutions simultaneously. However, the Gaofen-1 …

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

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

In recent years, the application of deep learning to change detection (CD) has significantly
progressed in remote sensing images. CD tasks have mostly used architectures, such as …

Haze obscures remote sensing images, making it difficult to extract valuable information. To
address this problem, we propose a fine detail extraction network that aims to restore image …

Optical remote sensing serves as a critical technology for exploring underwater
environments. However, light absorption and scattering underwater significantly degrade …

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