Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred
backgrounds, which has a wide range of applications such as traffic management and …

Although deep neural networks enable impressive visual perception performance for
autonomous driving, their robustness to varying weather conditions still requires attention …

Abstract Few-Shot Learning (FSL) is a challenging task, ie, how to recognize novel classes
with few examples? Pre-training based methods effectively tackle the problem by pre …

The visibility and contrast of images captured in adverse weather such as haze or fog
degrade dramatically, which further hinders the accomplishment of high-level computer …

Abstract Recent developments in Convolutional Neural Networks (CNNs) have made them
one of the most powerful image dehazing methods. In particular, the Residual Networks …

Atmospheric haze limits the performance of the camera sensor, which results in capturing
degraded hazy images. Removal of this haze from the observed images is a complicated …

Single image dehazing is a highly challenging ill-posed problem. Existing methods
including both prior-based and learning-based heavily rely on the conceptual simplified …

Forecasting physical signals in long time range is among the most challenging tasks in
Partial Differential Equations (PDEs) research. To circumvent limitations of traditional …

In this paper, a image processing method called VRHI is developed to enhance single hazy
images. More specifically, inspired by visual characteristics of haze, a haze density …