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 …

Large-scale deployed cameras in the automated container terminal (ACT) area helps on-site
staff better identify unexpected yet emergency events by monitoring port personnel …

Snowfall is a common weather phenomenon that can severely affect computer vision tasks
by obscuring objects and scenes. However, existing deep learning-based snow removal …

Images taken on snowy days often suffer from severe negative visual effects caused by
snowflakes. The task of removing snowflakes from a snowy image is known as image …

Restoring images captured under adverse weather conditions is a fundamental task for
many computer vision applications. However, most existing weather restoration approaches …

Convolutional neural networks-based video de-raining methods commonly rely on dense
intensity frames captured by CMOS sensors. However, the limited temporal resolution of …

Current video enhancement approaches have achieved good performance in specific rainy,
hazy, foggy, and snowy weather conditions. However, they currently suffer from two …

Existing deraining methods focus mainly on a single input image. However, with just a single
input image, it is extremely difficult to accurately detect and remove rain streaks, in order to …

This paper presents a correlation filter object tracker based on fast spatial-spectral features
(FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video …

Maritime ship detection plays a crucial role in smart ships and intelligent transportation
systems. However, adverse maritime weather conditions, such as rain streak and fog, can …