Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …

Remote sensing object detection (RSOD) encounters challenges in complex backgrounds
and small object detection, which are interconnected and unable to address separately. To …

Images captured in snowy days suffer from noticeable degradation of scene visibility, which
degenerates the performance of current vision-based intelligent systems. Removing snow …

Latest diffusion-based methods for many image restoration tasks outperform traditional
models, but they encounter the long-time inference problem. To tackle it, this paper …

Transformer-based image restoration methods in adverse weather have achieved significant
progress. Most of them use self-attention along the channel dimension or within spatially …

Adverse weather conditions, such as rain, raindrop, snow and haze, consistently degrade
images in an unpredictable manner, thereby rendering existing task-specific and task …

Image restoration under severe weather is a challenging task. Most of the past works
focused on removing rain and haze phenomena in images. However, snow is also an …

The ability to accurately identify tomato leaves in a field setting is crucial for achieving early
yield estimation, particularly with the growing importance of Precision Agriculture. It may be …

The outdoor vision systems are frequently contaminated by rain streaks and raindrops,
which significantly degenerate the performance of visual tasks and multimedia applications …

Noise introduced due to weather can reduce the efficiency of computer vision applications
as the visibility of the objects in images is greatly affected. Haze and rain are the most …