Single image dehazing is a challenging ill-posed problem due to the severe information
degeneration. However, existing deep learning based dehazing methods only adopt clear …

Deep learning-based methods have achieved remarkable performance for image dehazing.
However, previous studies are mostly focused on training models with synthetic hazy …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

In this paper, we propose a novel image dehazing framework with frequency and spatial
dual guidance. In contrast to most existing deep learning-based image dehazing methods …

We present a comprehensive study and evaluation of existing single-image dehazing
algorithms, using a new large-scale benchmark consisting of both synthetic and real-world …

For all the ways convolutional neural nets have revolutionized computer vision in recent
years, one important aspect has received surprisingly little attention: the effect of image size …

Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …

Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

We present a comprehensive study and evaluation of existing single image deraining
algorithms, using a new large-scale benchmark consisting of both synthetic and real-world …

In the past half of the decade, object detection approaches based on the convolutional
neural network have been widely studied and successfully applied in many computer vision …