Nighttime image dehazing is a challenging task due to the presence of multiple types of
adverse degrading effects including glow, haze, blur, noise, color distortion, and so on …

Existing deep learning-based computer vision methods usually operate in the spatial and
frequency domains, which are two orthogonal\textbf {individual} perspectives for image …

Video restoration networks aim to restore high-quality frame sequences from degraded
ones. However, traditional video restoration methods heavily rely on temporal modeling …

The ascension of Unmanned Aerial Vehicles (UAVs) in various fields necessitates effective
UAV image segmentation, which faces challenges due to the dynamic perspectives of UAV …

Single-image snow removal aims to restore clean images from heterogeneous and irregular
snow degradations. Recent methods utilize neural networks to remove various degradations …

Learning scene semantics that can be well generalized to foggy conditions is important for
safety-crucial applications such as autonomous driving. Existing methods need both …

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects
of fog on scene properties and the limited training data. Existing research has mainly …

Removing snow from a single image poses a significant challenge within the image
restoration domain, as snowfall's effects are in various scales and forms. Existing methods …

All-in-one adverse weather removal is an emerging topic on image restoration, which aims
to restore multiple weather degradations in a unified model, and the challenges are twofold …

The advancement of fingerprint research within public academic circles has been trailing
behind facial recognition, primarily due to the scarcity of extensive publicly available …