In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …

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 …

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated
remarkable success in low-level vision tasks, such as image super-resolution, deraining …

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

Rain streaks and raindrops are two natural phenomena, which degrade image capture in
different ways. Currently, most existing deep deraining networks take them as two distinct …

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 …

This survey article is concerned with the emergence of vision augmentation AI tools for
enhancing the situational awareness of first responders (FRs) in rescue operations. More …

This paper addresses the problem of robust deep single-image reflection removal (SIRR)
against adversarial attacks. Current deep learning based SIRR methods have shown …

Haze severely degrades the visibility of scene objects and deteriorates the performance of
autonomous driving, traffic monitoring, and other vision-based intelligent transportation …