Images captured under adverse weather conditions often suffer from blurred textures and
muted colors, which can impair the extraction of reliable information. Image defogging has …

Existing research based on deep learning has extensively explored the problem of daytime
image dehazing. However few studies have considered the characteristics of nighttime hazy …

Restoring high-quality images from degraded hazy observations is a fundamental and
essential task in the field of computer vision. While deep models have achieved significant …

Under hazy weather condition, images captured by electronic imaging devices frequently
encounter a number of issues, such as blurring of image details and the poorly defined …

While the wisdom of training an image dehazing model on synthetic hazy data can alleviate
the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain …

In this paper, we propose a novel dehazing method based on self-distillation. In contrast to
conventional knowledge distillation approaches that transfer large models (teacher …

Self-supervised learning methods have demonstrated promising benefits to feature
representation learning for image dehazing tasks, especially for avoiding the laborious work …

On the one hand, the dehazing task is an ill-posedness problem, which means that no
unique solution exists. On the other hand, the dehazing task should take into account the …

Recent unsupervised image dehazing methods used unpaired real-world training data for
enhancing generalization on real-world scenes. However, these methods often require …

Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge
models are trained in synthetic data, leading to the poor performance on real-world hazy …